Important Notice

2020/04/24 Update: We have closed the call for applications for PFN Summer Domestic Internship 2020. Thank you for many applications.

2020/04/23 Update: Due to COVID-19, we’ve decided to hold this year’s domestic summer internship program remotely. After reviewing the feasibility of each theme, we concluded that it would be difficult to hold “19. Development of Modules for Human Pose Estimation” remotely and cancelled it.

This program is only for students who already have visa eligibility to work in Japan as an intern this summer. Applications for 2020 global internships (those who need visa support to work in Japan) are being accepted separately till March 30, 2020. For details, please visit https://www.preferred.jp/en/news/internship2020global/ (Closed).

Note that depending on the situation of the COVID-19 infection in Japan in the future, we may take measures such as canceling the program or changing the schedule and contents of the program.

Overview

Preferred Networks (PFN) is looking for enthusiastic interns who can work with us in our Tokyo office this summer. Students who applied for previous internships are also eligible to re-apply (except those who previously interned with PFN). We welcome students who want to develop new technologies, software, and services across a wide range of computer science areas (including machine learning) with us.

Index

Characteristics and Duration of PFN Internship

Theme List

Key Qualifications

Important Notes before You Apply

How to Apply

Selection Process

Basic Employment Conditions and Benefits

Characteristics and Duration of PFN Internship

• Over the 1.5 month period, PFN researchers and engineers will be assigned to work with you as a mentor. You will have opportunities to discuss, study, and develop in your internship theme with specialists in various fields including deep learning, computer vision, robotics, bio-healthcare, reinforcement learning, and distributed processing.
• We encourage the results of the internship to be published as OSS, papers, and PFN Research & Development blogs if there are no confidentiality or rights issues.  

• Start date：Tuesday, August 11th, 2020  (Negotiable)
• End date： Friday, September 18th, 2020
  • You can choose to continue to work in the week of September 23rd – 30th under the same terms and conditions.
    • This year’s internship will end on September 18th in principle in consideration of the fact that many schools start their fall semester in late September. 
    • If the above case does not pertain to you and you need more time to finalize your research or want to spend more time with our staff, you can extend your internship until September 30th under the same terms and conditions. 
    • We understand you may have to be absent for (e.g.) lab activities, attending academic conferences, and returning home for family commitments. We are flexible about your need to take days off due to these reasons.

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Theme List

• For the 2020 Domestic Internship at PFN, we have prepared the themes listed further below for both engineering interns and research interns:
  • Engineering intern: who emphasizes development that does not necessarily aim for research presentation (re-implementation of dissertation, development of OSS, etc.)
  • Research intern: who aims to conduct research more autonomously and publish the results as a paper
• During the selection process or after completion, you will discuss with our members to finalize your theme. You can choose up to 2 themes in the application form. 
• Any intellectual properties achieved through your internship activities shall belong to PFN.  Therefore, upon determination of intern theme, please ensure not to bring your own research theme at the research lab at your university or other organization you belong to.

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Engineering

[+] 1. Research and Development on Data Science / Machine Learning Application

Research and Development on Data Science / Machine Learning Application

Must Requirements

・Broad knowledge on machine learning
・Skill and experience of literature survey and software implementation

Want Requirements

・Experience of winning a data analysis competition such as Kaggle
・Knowledge and experience on outlier detection / anomaly detection
・Contribution to any open source software or engineering skill to develop software libraries

[+] 2. Development of a Framework or Libraries for Executing Deep Learning Models on Actual Products

① Development of Framework for High-speed Executing Deep Learning Models on a Smartphone and Other Edge Devices

Must Requirements

・Experience with implementation of deep learning models
・Experience with implementation of an application for a smartphone (or other devices)

Want Requirements

・Knowledge in ONNX, MLIR
・Experience with optimization for machine learning models and source code.
・Knowledge in vendor tools such as SNPE and so on.

② Development of Obfuscation Tools

Must Requirements

・Excellent results of CTF rev/pwn
・Deep knowledge of linker (cf: have an ability to complete https://github.com/shinh/sold)

Want Requirements

None

[+] 3. Development and Application of Differentiable Graphics and Rendering

① Development of a High-Quality Library of Differentiable Graphics and Rendering

Must Requirements

・Experience with Python
・Experience with implementation of deep learning systems
・Knowledge in 3D computer vision or computer graphics
・High skill in software development

Want Requirements

・Experience with PyTorch
・Experience with CUDA programming
・Experience with software development in a team
・Open source development contribution

② 3D Reconstruction of Large Scene Using Differentiable Rendering Engine

Must Requirements

・Python coding skill
・Experience on developing image recognition system using deep neural networks

Want Requirements

・Experience and knowledge on 3D computer vision
・Experience on training deep neural networks using many GPUs

[+] 4. Research and Development on Image Recognition

① NAS for Image Recognition

Must Requirements

・Python coding skill
・Experience on developing image recognition system using deep neural networks

Want Requirements

・Experience with PyTorch
・Experience writing papers or externally presenting articles on computer vision and machine learning.

② Image Recognition with Minimal Annotation Effort

Must Requirements

・Python coding skill
・Experience on developing image recognition system using deep neural networks

Want Requirements

・Experience with PyTorch
・Experience writing papers or externally presenting articles on computer vision and machine learning.

[+] 5. Development of Object Recognition System Using 3D Data

① Object Surface Material Estimation on 3D Scanner

Must Requirements

・Python coding skill
・Experience on developing image recognition system using deep neural networks

Want Requirements

・Experience and knowledge on 3D computer vision

② Training Image Recognition Model Efficiently by Using CG Images

Must Requirements

・Python coding skill
・Experience on developing image recognition system using deep neural networks

Want Requirements

・Experience and knowledge on 3D computer vision
・Experience on using 3D game engines or modeling tools like Unity or Blender

③ Developing Annotation Tools for 3D Data

Must Requirements

・Python coding skill
・Experience on using 3D game engines or modeling tools like Unity or Blender

Want Requirements

・Experience and knowledge on 3D computer vision

[+] 6. High-Performance Data Communication Network for Parallel and Distributed Deep Learning

High-Performance Data Communication Network for Parallel and Distributed Deep Learning

Must Requirements

・Fundamental knowledge on TCP/IP and datacenter network
・System programming skills
・Fundamental knowledge of operating systems and PC architecture

Want Requirements

・Knowledge on RDMA and RDMA over Converged Ethernet (RoCE)
・Experience on network device (Ethernet switches) configuration
・Network monitoring skills (Streaming Telemetry / Netflow / IPFIX)
・Understanding of data communication and collective operations in parallel and distributed deep learning
・Fundamental knowledge on system observability and data collection/visualization tools (e.g. Prometheus)

[+] 7. Development of Compiler Backend for MN-Core

Development of compiler backend for MN-Core

Must Requirements

・Knowledge on computer architectures
・Knowledge of compiler backend
・Enough experience in C++

Want Requirements

・Knowledge in basic algorithms and workflow of machine learning / deep learning
・Knowledge in APIs of deep learning flameworks
・Experience with optimization on accelerator

[+] 8. Development of System Software for MN-Core

Development of system software for MN-Core

Must Requirements

・Experience in system programming
・Knowledge on computer architectures and operating systems
・Knowledge and implementation experience in concurrent / parallel processing

Want Requirements

Knowledge / experience in performance measurement and monitoring

[+] 9. Development of a Library for Multiple-types of Biological Data Analysis

Development of a Library for Multiple-types of Biological Data Analysis (e.g., DNA/RNA Sequences and Phenotype)

Must Requirements

・Python coding skill (Fluency in Python)
・Basic knowledge of biology (undergrad level)

Want Requirements

・Experience of development tools (e.g., git)
・Experience of team development.
・Experience of using biological data analysis libraries

[+] 10. Application Research or Development of Machine Learning or Molecular Simulation in Drug Discovery

Application research or development of machine learning or molecular simulation in drug discovery

Must Requirements

・Python coding skill
・Knowledge on Chemoinformatics
・Basic knowledge on Biology & Chemistry

Want Requirements

・C++ coding skill
・Project level experience with parallel computation using MPI, etc.
・Experience with RDKit library
・Experience with deep learning (using PyTorch, etc.)
・Experience of MD simulation of biopolymer & free energy calculation

[+] 11. Deep Learning-based Atomic simulation and Its Application to Material Development

Deep learning based atomic simulation and its application to material development

Must Requirements

・Python coding skill
・Knowledge about atomic simulation (e.g., molecular dynamics, reaction pathway analysis, chemical property calculation / optimization, etc.)

Want Requirements

・For those who are interested in the application : A suggestion for specific target system or phenomena is preferable.
・For those who are interested in simulator development : A knowledge on recent deep learning for atomic simulation and experience on deep learning implementation and/or a suggestion for specific tasks to solve is preferable.
・Experience at conferences or writing papers
※We do not limit target system, including metal, semiconductor, battery, ceramics, organic molecular system, etc. Also, we do not limit target phenomena. Any engineering fields, including chemical reaction process, mechanical properties, etc., are applicable.

[+] 12. Application of deep learning techniques to animation

Application of deep learning techniques to animation

Must Requirements

・Python coding skill
・Experience with implementation of deep learning systems

Want Requirements

・Experience with PyTorch or Chainer
・Experience with implementation of deep generative models
・High skill in software development
・Interest in anime, etc.

[+] 13. Development of Simulator Developing Technologies and Simulator-related Technologies for Industrial Process

Development of simulator developing technologies and simulator-related technologies for industrial process

Must Requirements

・Programming skills (e.g., Python, C/C++)
・Experience on system programming

Want Requirements

・Knowledge and experience of numerical analysis
・Excellent skill in software development
・Experience of compiler / interpreter implementation
・Knowledge and experience of HPC (in particular CUDA programming)

[+] 14. Application of Reinforcement Learning to Robotics

Application of reinforcement learning to robotics

Must Requirements

・Knowledge and experience in reinforcement learning and deep learning
・Experience in programming with robots
・Python coding skill

Want Requirements

・Excellent skill in software development
・Knowledge in robotics
・Experience in PyTorch or Chainer
・Research experience in machine learning

[+] 15.Computer Vision for Autonomous Driving

Computer vision for autonomous driving

Must Requirements

・Knowledge about deep learning-based computer vision
・Solid understanding of linear algebra, machine learning
・Experience with implementing deep learning algorithms
・Great Python coding skill, knowledge of software development best practices, debugging / profiling

Want Requirements

・Experience with PyTorch
・Experience with a GPU tensor processing library such as CuPy/ATen
・Familiar with typical data science environment such as JupyterLab / Jupyter Notebook, k8s, bash scripting, Linux
・Experience with scaling up deep learning experiments to multiple GPUs
・Experience working with high resolution images or videos on a limited memory GPU
・Published a computer vision paper as the first author

[+] 16. Development of Optuna

Development of Optuna

Must Requirements

・Experience with developing/contributing to OSS or equivalent

Want Requirements

・Strong track records of ML such as publication, competition, etc.
・Domain knowledge of Bayesian optimization and/or automatic machine learning

[+] 17. Development of Distributed Reinforcement Learning Technology

Reproduction and Improvement of Distributed Reinforcement Learning Code

Must Requirements

Implementation experience of training code using distributed learning

Want Requirements

・Experience with Python
・Implementation experience of parallel algorithm using multi-node
・Implementation experience of reinforcement learning algorithm

[+] 18. Development of Related Localization Technology

① Image Retrieval Technology Development for Localization

Must Requirements

・Advanced knowledge of machine learning and computer vision
・Experience of machine learning framework (PyTorch and Chainer is preferred)

Want Requirements

・Experience writing papers or externally presenting articles on computer vision and machine learning
・Image retrieval technology development experience

② Development of Direct / Indirect Hybrid SLAM

Must Requirements

・Advanced knowledge of machine learning and computer vision
・Experience of SLAM development and implementation

Want Requirements

・Experience writing papers or externally presenting articles on computer vision and machine learning
・Advanced coding skills

③ Localization Technology Development Using 3dLiDAR

Must Requirements

Advanced knowledge of machine learning and computer vision

Want Requirements

・Experience writing papers or externally presenting articles on computer vision and machine learning
・Image retrieval technology development experience

[+] [Cancelled] 19. Development of Modules for Human Pose Estimation

Due to COVID-19, we’ve decided to hold this event remotely. After reviewing, we concluded that it would be difficult to hold this theme remotely and decided to cancel it. We will contact those who already applied for this theme over email individually.

Development of Modules for Human Pose Estimation from Perspective Viewpoint by Fisheye Lens

Must Requirements

・Experience with implementation of deep learning models
・Experience with 3D rendering

Want Requirements

・Advanced knowledge in computer vision

[+] 20. Quantitative Finance Using Machine Learning

【Engineering】Quantitaive Finance using Machine Learning

Must Requirements

・Basic knowledge of machine learning and finance

Want Requirements

・High expertise in finance-friendly fields

Research

[+] 21. Machine Learning-based Physical Simulation

Machine Learning based Physical Simulation

Must Requirements

・Advanced knowledge of machine learning
・Experience of machine learning framework (PyTorch and Chainer is preferred)
・Basic knowledge of physics or chemistry

Want Requirements

・Experience of building simulation model
・Knowledge and experience of numerical analysis

[+] 22. Research on the Current Medical Image Problems Such As Practical Use of Few Samples and/or Evaluation of the Difference between Facilities / Machines

Research on the Current Medical Image Problems Such As Practical Use of Few Samples and/or Evaluation of the Difference between Facilities / Machines

Must Requirements

・Knowledge of computer vision
・Skills of deep learning frameworks (e.g. Chainer, TensorFlow, Keras, PyTorch, MXNet)
・Programming skills (e.g. Python, C/C++)

Want Requirements

・Knowledge of biology equivalent to at least undergraduate level
・Experience of research with images or video data
・Publishing papers in the field of biology, mathematics, or informatics
・Experience developing applications using machine learning or deep learning

[+] 23. Machine Learning Research: Deep Learning towards Society

Machine learning research: Deep learning towards society

Must Requirements

・Publishing record in top-tier conferences and/or journals as a main contributor
・In-depth understanding of machine learning in general

Want Requirements

・Applicant shall preferably be a Ph.D candidate or equivalent.
・Applicant is expected to have had some implementation experience of deep learning frameworks like Chainer and Pytorch.
・Applicant shall apply with his/her own research proposal. The proposal MUST NOT have any overlap with any project in the ongoing graduate program
・Applicant is expected to be well versed with the theoretical background of the machine learning field of his/her interest

[+] 24. Quantitative Finance Using Machine Learning

[Reseach] Quantitaive Finance Using Machine Learning

Must Requirements

・Advanced knowledge of machine learning and finance

Want Requirements

・High expertise in finance-friendly fields.
・Experience writing papers on finance.

[+] 25. Next-generation Architectures for Highly Efficient Learning and Inference

Research on Next-generation Architectures for Highly-efficient (Deep Learning) Training and Inference

Must Requirements

・Basic knowledge of deep learning and inference
・Solid knowledge of computer architecture
・HDL coding skills

Want Requirements

・Actual design experience
・Experience using EDA tools
・Experience in architecture research

[+] 26. Machine Learning and EDA

Research on Machine Learning Applications in EDA (Electronic Design Automation)

Must Requirements

・Expertise in machine learning
・Experience using EDA tools
・Basic knowledge of EDA algorithms

Want Requirements

・Experience in EDA research
・Experience using/applying machine/deep learning

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Key Qualifications

• PFN is seeking highly motivated and skilled individuals who can develop applications, tools, etc., independently. 
• Knowledge and development experience based on the themes listed in [Theme List] will be considered in the selection process. Please check each theme for details. The common requirements are:
  • Currently enrolled in high school, technical college, university, or graduate school. Negotiable for those attending other higher education institutions
  • Fluent in Japanese or English
  • Strong communication skills
  • Can work from our Tokyo office on weekdays
    (we are not accepting remote working for the internship program currently)
• Do not hesitate to apply even if you don’t have prior experience in full-scale development.

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Important Notes before You Apply

• Non-Japanese students who are studying at Japanese universities with a student visa must make sure to get【Permission for Other Activity / 資格外活動許可】before the start date of your PFN internship program.
• You need to let us know in advance if any administrative work is required to receive academic credit from your school. Please note that depending on the complexity of the work, PFN may not be able to accommodate your request.

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How to Apply

• Click this application form to apply. To access the application form, you will need to log in with a Google account.  We use your personal information filled in the application form for selection of summer interns.

• About submission of your portfolio
  • Summarize your skills and qualifications freely in an A4 paper to pitch yourself. 
  • Highlight and showcase some of your best work such as software you have developed, published papers, awards or prizes you have received, programming contests you have participated in, your blog, your sites, Twitter account, and other social media sites. 
  • Please upload it using the “Portfolio to pitch yourself” field in the application form.
• Deadline：Friday, April 24th, 12:00 PM JST (No late submission allowed)
• For inquiries:intern2020-admin@preferred.jp
  • An online Q&A session will be set separately soon. Please follow our Twitter account (@PreferredNet) to check the details!

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Selection Process

First screening

• After we close the application on April 24th, we will send the coding tests to all applicants in principle on the same day after 18:00.
• The deadline for submitting coding tests is May 8th, 12:00 PM JST.
• The result of the first screening will be sent after May 22nd.

Interviews

• One interview, in principle.  Additional interviews may also be required depending on the situation.
• Interview will be held in the PFN Tokyo Office. Remote interview via Zoom is also available for candidates in distant areas.
  ※Some or all interviews may be conducted online due to the COVID-19.
• The interview will take place within 4 weeks from May 25th. 

Offer

• The notification of the overall interview result will be given on June 29th or later.

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Basic Employment Conditions And Benefits

• Location
  • PFN Tokyo Office
  • Otemachi Bldg., 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004

• Salaries：
  • 2,500 yen an hour for technical college, university, graduate school students
  • 2,000 yen an hour for high school students
• Work hours：8 hours in principle. 5 days a week excluding Saturdays, Sundays, and public holidays.  
• Commuting fee support：PFN will pay for your daily commute to and from the office in an approved route.
• Travel cost：For students traveling a long distance by plane or Shinkansen to participate in the internship, PFN will support the cost for one round trip to relocate between the Tokyo area and the place where you’re currently living.
• Accommodation support：For students coming from distant areas, PFN will provide an accommodation allowance of 5,000 yen per day for the entire period of your internship including holidays. 
  • You need to arrange a place to stay by yourself. Reasonable weekly rental apartments are available near the PFN office ranging from 100,000 to 150,000 yen a month. 
  • Please note that the accommodation allowance is taxable.

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投稿 Call for Applications for PFN Summer Internship 2020 は Preferred Networks, Inc. に最初に表示されました。

Message to candidates who’ve applied:

Thank you for applying for PFN 2020 Summer-autumn Global Internship.

People are most crucial for PFN. That’s why we’re monitoring the situation regarding SARS-COV-2/COVID-19 closely and are considering all appropriate responses to the situation. The global situation regarding the virus has changed, and as the virus continues to spread across the world, we have to consider significant changes to the internship schedule or even a complete cancellation.

As your health and safety is the highest priority for us, at this point, we cannot exclude the possibility of rescheduling (or even canceling) the internship. Also our response will be slightly behind schedule due to the current situation.

Of course, we will inform all candidates who are currently in selection process regarding any steps we might take to deal with the situation in Japan and abroad.

Index

• Overall
• Minimum Required Experience & Skills
• Topics for this year
  • Topic 1: Next-generation chip architecture for deep learning
  • Topic 2: SLAM, SfM, and Depth Estimation
  • Topic 3: NN-based fast physics simulation
  • Topic 4: Machine learning models with inter-domain transferability
  • Topic 5: High-Performance Data Communication Network for Parallel and Distributed Deep Learning
• Selection Process & Schedule
• How to Apply
• Working Environments & Benefits

Overall

What is PFN’s Global Internship Program

Preferred Networks (PFN) will be organizing an internship program for summer/autumn 2020 in our Tokyo headquarters.

PFN’s central goal is to make the real world computable. This, of course, is an enormous challenge requiring world-class research. As part of this research effort, we also want to involve extremely talented students to join us in realizing this goal.

As a part of our previous internships, many of our interns have published their work in top-tier conferences such as ICLR, ICML, NeurIPS and ICRA.

We are looking for self-motivated, energetic interns who are eager to boldly explore where no one has ventured before. As an intern, you will be working closely with our teams to conduct research in one of the following five topics (refer to [Topics for This Year] section) that are focused on industrial applications.

About PFN

PFN is a company specializing in the industrial application of machine learning. As the leading AI startup company in Japan, PFN employs over 270 specialists with the aim of developing new machine learning technologies and cutting edge solutions to challenging real-world problems. (https://www.preferred.jp/en/)

Why you should intern at PFN?

PFN provides interns with an attractive research environment.

• Variety of Expertise
  There are talented and experienced researchers from various backgrounds as well as software engineers who have developed the deep learning framework, Chainer, and relevant software libraries including CuPy, Optuna, Menoh, and Chainer libraries. However, PFN’s ambition is not limited to software, but we also have hardware researchers and engineers working on the next generation of chips, clusters and robots. We are conducting interdisciplinary research with these experts in various research fields such as robotics, networking, bio, and chemistry.
• Powerful Computing Resources
  PFN operates three clusters with a combined computing power of 200 PetaFLOPS equipped with 1024 V100 GPUs, 512 V100 GPUs, and 1024 P100 GPUs, respectively. One of the clusters is ranked and 1st in Japan (12th in the world) among industrial supercomputers in the TOP500 List (http://www.top500.org) in 2017. These clusters are utilized for our research activities, including ImageNet 15min in 2017 and won 2nd prize at object detections in Google Open Images Challenge 2018.
• Access to High-end Robots
  In our robotics research, we use a large number of mobile robots including Toyota HSR (Human Support Robot).  We are developing various hardware and software to make the operation of industrial manipulators more efficient.
• Past achievements (selection)
  cf.  Best Paper Award Finalist in ICRA 2019
        Best Paper Award on Human-Robot Interaction in ICRA 2018
        Honorable Mention Award in CHI 2019
        Honorable Mention Award in CHI 2018
        3rd prize at Kaggle Google AI Open Images Challenge 2019 (Instance Segmentation track)
        2nd prize at Kaggle Google AI Open Images Challenge 2018 (Object Detection track)
        Training ImageNet in 15 min (2017)
        2nd Prize at Amazon Picking up Challenge (2016)

Publications from Previous Internships

• Einconv: Exploring Unexplored Tensor Decompositions for Convolutional Neural Networks, Kohei Hayashi et al., NeurIPS 2019.
• A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation, Mitsuru Kusumoto et al., NeurIPS 2019.
• Robustness to Adversarial Perturbations in Learning from Incomplete Data, Amir Najafi et al., NeurIPS 2019.
• Other than the above, 4 papers in NeurIPS 2019 workshops.
• Dynamic Task Control Method of a Flexible Manipulator Using a Deep Recurrent Neural Network, Kento Kawaharazuka et al., IROS 2019.
• Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network, Kento Kawaharazuka et al., ICRA 2019.
• A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning, Yoshihiro Nagano et al., ICML 2019.
• Learning Discrete Representations via Information Maximizing Self Augmented Training, Weihua Hu et al., ICML 2017.
• Neural Multi-scale Image Compression, Ken Nakanishi et al., ACCV 2018.
• Distantly Supervised Road Segmentation, Satoshi Tsutsui et al., ICCV Workshops 2017.

Internship Period

• Earliest start date: Aug 17th, 2020
• The period of the internship can be flexibly arranged as following:
  •  Start date you can chose: Middle August, Late August or Early September
  •  Ending date you can chose: Middle November, Early to Late December or later than that.
• A minimum duration of twelve weeks.

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Minimum Required Experience & Skills

• Formally enrolled in a master’s or PhD program at a university or research institute outside of Japan at the time of application and during the internship.
  Note:
  • While we expect PhD students to apply, exceptional master students are also encouraged to apply.
  • A graduate certificate is required at the time of acceptance of the internship offer to start visa application
• Fluent in either English or Japanese.
• Able to work full-time at our Tokyo office for the duration of the internship period.
• Additionally, please see each topic description for concrete requirements.

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Topics for this year

Topic 1: Next-generation chip architecture for deep learning

Description

Deep learning requires extreme amounts of compute and comes with huge challenges. At PFN, we aim to provide world-class solutions to enable our engineers and researchers to compute more with less. This is exemplified by our current generation MN-Core accelerator, providing 524TFlop/s half-precision per card while consuming less than 1 W per Teraflop in half-precision. If you’re into deep learning, but call computer architecture/engineering, semiconductor technology or related fields your home, come and research the next-next generation computer architecture for deep learning with us. Let’s pave the way for the future of deep learning research!

Required Experience & Skills

• Knowledge and skills: 
  • Understanding of deep learning algorithms and popular neural network architectures. Big plus if the person has experience in optimizing computer architectures towards such networks.
  • Practical validation skills to show the actual merit of proposals (RTL coding, use of synthesis tools, solid understanding of chip design, usage of simulation environments, solid understanding of timing and power prediction tools and theory, etc.)
  • A solid understanding of architecture or architecture related research (processor-like / RISC-V, reconfigurable computing, domain-specific architectures/languages, applied semiconductor research e.g. post-silicon, compute architecture related research, especially in regard to deep learning applications)
• Solid publication record: First author publication(s) in related conferences, big plus for publications in DAC / DATE / ASP-DAC / ICCAD, MICRO / ISCA, ISSCC, SC / ISC, VLSI, … 
• Big plus: Actual design experience

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Topic 2: SLAM, SfM, and Depth Estimation

Description

At PFN, we want to ‘make the real world computable’, i.e., enable computational gadgets to interact with the real world. Understanding the geometric structure of the environment is a crucial ingredient for achieving that goal. In particular, localization, mapping, and depth understanding are fundamental capabilities that are necessary for autonomous robots/applications to successfully perform real world tasks. In this internship, we are looking for talented and motivated candidates who want to push the research frontier in these areas forward.

Recent works such as [1] show that deep learning infused with traditional optimization strategies can improve existing localization and mapping methods. In contrast, [2] and its predecessors formulate a completely new deep learning based approach for monocular visual localization. Finally, we are also seeing unmatched performance in depth estimation by novel deep learning based methods [3]. Motivated by this, we want to challenge, improve, and innovate in these domains with skilled interns.

Reference:

[1] Unsupervised Collaborative Learning of Keyframe Detection and Visual

Odometry Towards Monocular Deep SLAM, Lu Sheng et al, ICCV 2019

[2] From Coarse to Fine: Robust Hierarchical Localization at Large Scale, Paul-Edouard Sarlin et al, CVPR 2019

[3] Digging into Self-Supervised Monocular Depth Prediction, Clément Godard et al, ICCV 2019

Required Experience & Skills 

• Previous experience with research: The candidate is expected to have worked on projects which highlight his/her ability as a researcher. This includes projects which have led to publications in top-tier conferences such as: CVPR, ICCV, ECCV, ICLR, ICML, NeurIPS, ICRA, IROS.
• Strong coding skills: The candidate is expected to have experience in implementing computer vision projects in Python/C++. This includes familiarity with Deep Learning libraries such as TensorFlow, PyTorch, or Chainer.

Preferred Experience & Skills

• Knowledge: Strong understanding of:
  • Deep Learning (such as deep architectures for depth estimation, semantic understanding), 
  • Classical Computer Vision (related to SLAM, SfM, stereo matching etc.), 
  • Optimization Methods (such as bundle adjustment, Iterative Closest Point).
• Awareness of current trends: Knowledge of recent methods working towards fusing Deep Networks with Classical Methods, such as CNN-SLAM, or View Extrapolation with Multi Plane Images.

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Topic 3: NN-based fast physics simulation

Description

The goal of our company is to “make the real world computable” by improving our understanding of the real world and using the knowledge to regulate/optimize the systems. In order to achieve this goal, we need a good simulator for various real-world processes, ranging from macroscopic dynamics like climate change to atomic interactions. While the development of all these simulators are equally difficult in their own ways, they all share the same challenge; the problem of ill-definedness, or the problem of model selection. The only way to resolve this problem is to introduce further inductive biases or regularization functions. We are looking for an ambitious intern who is interested in developing innovative methods of simulation to tackle this problem—either in specific areas of science or in simulation more generally. This internship project will be supported by our powerful computing environments, allowing us to run massively parallel simulations or heavy first-principles calculations.

Area of interests: Protein folding, Chemical reactions, Geological science, Material science, Plant control

Required Experience & Skills

• Solid publication record as the first author in one or more areas of interest

Preferred Experience & Skills

• Solid understanding of statistical learning / machine learning
• Background knowledge of the target area
• Strong Coding skills: The candidate is expected to have excellent skills in Python/C++ implementation, possibly demonstrated in his/her GitHub projects.

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Topic 4: Machine learning models with inter-domain transferability

Description

With deep models, big-data, and high-performance computers, today’s advanced supervised learning methods allow us to perform well on the domain from which the training data was obtained. On the other hand, models trained with classical supervised methods often generalize poorly when making out-of-domain predictions. The inconvenient truth is that, in real-world applications, models are often used in the ways there were not originally intended, and are often fed with out-of-domain datasets. For instance, it is unlikely that all CT scan images are taken under the exact same measurement condition with the exact same machine, and reinforcement learning algorithms suffer from simulation-to-reality gap.

Recently, this problem is drawing greater attention in the machine learning community, and we also believe that this is an important problem in our goal of “making the real world computable.” Recent efforts include the development of models that are capable of fast adaptation /domain-transfer/Sim2Real. During this internship, you will have the opportunity to apply these various techniques on PFN’s many robots. We are looking for a self-motivated, energetic intern who is interested in exploring innovative ways to tackle this problem.

Required Experience & Skills

Solid publication record at top-tier AI conference such as ECML, ICML, ICLR, AISTATS, ICRA, IROS, ICCV, ECCV, CVPR, and NeurIPS as the first author

Preferred Experience & Skills

• Solid understanding of statistical learning and deep learning
• Implementation experiences of DL in relevant areas

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Topic 5: High-Performance Data Communication Network for Parallel and Distributed Deep Learning

Description

Preferred Networks (PFN) builds and operates an in-house supercomputer with our dedicated accelerator devices, called MN-Core, as well as general purpose graphics processing units (GPGPUs) for parallel and distributed deep learning. We are tackling various research challenges to achieve scalable, efficient, and operable deep learning infrastructure. This year’s internship program focuses on two key challenges: 1) data communication network enhancement, and 2) global system optimization of deep learning infrastructure.

Data communication networks are an indispensable component for communicating between multiple accelerators over the training process, and feeding data from storage to computing nodes. A research challenge is to enhance the data communication network to accelerate the parallel and distributed deep learning. Our focus includes remote direct memory access (RDMA) for low latency and high throughput and in-network computing for collective operation offload.

Global system optimization of shared resources such as storage and network I/O is also challenging in a multitenant system. This includes the improvement of the job scheduler and resource allocation (e.g., job packing and placement) for heterogeneous jobs, efficient resource utilization such as speculative execution in training data copy, and traffic engineering.

We are looking for a self-motivated intern who takes on these research challenges with enthusiasm.

Preferred Experience & Skills

• Knowledge
  • Fundamental knowledge on data communication networks
  • Understanding of data communication and collective operations in parallel and distributed deep learning
• Skills: System programming experience
• Solid publication record: Publication(s) as the first or corresponding author in related conferences, big plus for publications in ACM/IEEE Supercomputing Conference (SC), International Supercomputing Conference (ISC), ACM SIGCOMM, CoNEXT, USENIX ATC, NSDI

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Selection Process & Schedule

• Application with required documents
• Previous task before interview depends on topics
• 1~ 2 rounds of online interviews (includes online coding test depends on topics)
• Schedule:
  ※All the date and time are in Japanese Standard Time (JST: UTC+9)
  • Application period: 18:00p.m., Dec 16 2019 (JST) to 12:00p.m., March 30 (JST)
  • Selecting period: 

• Relocation 
  • visa application: from mid-May (visa process takes around 2~3 months)
  • other procedures for transportation, accommodation, etc.

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How to Apply

• Required documents:
  • Resume / CV (PDF format only. Please make sure your resume / CV does not contain  any personal or private information except name, email address, and affiliation)
  • A list of your publications
  • A research statement
    • Please also send a research statement that demonstrates a solid grasp of the field and understanding that showcases your ability to improve the state-of-the-art within the internship timeframe.
    • Content: Choose one important problem, explain why this problem is important and outline how you would solve that problem.
    • However, please note
      • If you write a new statement for something you’d like to do at PFN, this is also welcome, however, please keep it separate from your current (main) academic research to prevent complications with your University after the internship. 
      • This research statement is solely used to judge your ability to find interesting and impactful questions in the domain. 
  • GitHub account (if any)
• To apply:
  • Please fill the google forms (←click to apply) and submit your application 　
  • Deadline: 12:00 p.m., March 30th Monday, JST
    • No later applications will be accepted
    • Expected time to obtain a working visa  for Japan is between 2-3 months

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Working Environments & Benefits

Working time & Location

• 8 hours per day, 5 days per week (excluding national holidays)
• Location: Preferred Networks Tokyo office: Otemachi Bldg. 3F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
• https://www.preferred.jp/en/company/
• Contact: intern2020-admin@preferred.jp

Benefits

• Reimbursement of actual expenses for round trip from/to your home 
• Residential and living expense support in addition to salary
  • Note: Interns need to arrange accommodation by themselves. PFN staff can assist in providing relevant information.

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投稿 2020 PFN Global Internship Program Call for Applications は Preferred Networks, Inc. に最初に表示されました。

Preferred Networks (PFN) is looking for enthusiastic interns who can work with us in our Tokyo office this summer. Students who applied for previous internships are also eligible to re-apply (except those who previously interned with PFN). We welcome students who want to develop new technologies, software, and services across a wide range of computer science areas including machine learning with us.

Important notice:

Note: This program is only for students who already have visa eligibility to work in Japan as an intern this summer. We are not accepting applications from students who need support for obtaining the designated activity visa to work as an intern because the due date for processing such applications has already passed.

Characteristics of PFN Internship

• Over the two-month period, PFN researchers and engineers will be assigned to work with you as a mentor. You will have opportunities to discuss, study, and develop in your theme with specialists in various fields including deep learning, computer vision, natural language processing, robotics, bio-healthcare, reinforcement learning, and distributed processing.
• After the internship, you can make your research result public by writing a paper or making it OSS, to the extent to which is possible.  

Duration

• Start date： Early August, depends on your schedule
• End date： Sept. 20 (Fri), 2019
  • You can choose to continue to work in the week of Sept. 24-27 under the same terms and conditions.
    • This year’s internship will end on Sept. 20 in principle in consideration of the fact that many schools start their fall semester in late September.
    • If your school is not the case stated above and you need more time to finalize your research or want to spend more time with our staff, you can extend your internship until Sept. 27 under the same terms and conditions.
    • We understand you may have to be absent for e.g. lab activities, attending academic conferences, returning home for family commitments. We are flexible about your need to take day-off due to these reasons.

Theme List

• For 2019 Domestic Internship at PFN, we prepared the themes listed further below.
• During the selection process or after completion, you will discuss with our members to finalize your theme. You must at least choose your 1st and 2nd preferences in the application form. If you have more than two areas of interest, select a 3rd preference, which is optional.

Themes:

1. Theoretical research of ML/DL
2. Computer vision for 3D scene
   • Neural networks for 3D tasks (differentiable renderer, 3D reconstruction using neural network)
   • Development of SLAM and 3D reconstruction
   • Visual-SLAM
3. Computer vision on time-series dataset
   • Video analytics (sports, etc…)
4. Computer vision for other general topics
   • Object detection
   • Segmentation
   • Image classification
   • Few-shot learning
   • Image generation
5. Application of deep learning to Anime / Supportive software for creators
6. Reinforcement learning
7. Research and development on applications of machine learning algorithms such as mathematical optimization, simulation, and time-series prediction
8. Bio-healthcare
9. Chemoinformatics / Materials Informatics
10. Dialog, semantic parsing, symbol grounding, reasoning, or translation
11. Speech and signal processing
12. Interface and interaction
    • VR or AR
    • Human Computer Interaction / Human Machine Interaction, Human Robot Interaction
13. Robotics – DL/DRL for robotics
14. Robotics – Research and development of robot simulation
15. Robotics – Planning of mobile robots
16. Development of Chainer or libraries on Chainer
    • Development of Chainer
    • Development of CuPy
    • Development of area-specific libraries based on Chainer (such as ChainerCV, ChainerRL, ChainerChemistry and ChainerUI)
17. Research and development on performance optimization of machine learning, etc.
    • Optimization of NN models for inference
    • Development of compiler techniques for deep learning
    • Development of application software for our in-house developed accelerators
    • Development of processor, low-power computer architecture and VLSI technology for deep learning
18. Research and development of infrastructure for machine learning
    • HPC and distributed data management for distributed deep learning / deep learning
    • Development of experiment management systems, cluster management, experimental environment optimization for machine learning
    • Research and development of Edge Heavy Computing/In-Network Computing
    • Development of Optuna
19. Research and development of pipeline automation tools for machine learning
    • Topics related to automation of machine learning pipeline, including hyper parameter optimization, architecture search, and feature engineering
20. Front-end development
    • 【Renewal on April 5th】Development of information visualization tool, annotation tool / front-end for machine learning
    • Development of information visualization tool / front-end for machine learning
21. Product Design
    • Concept design, sketch and user interaction for robot products
    • Development of 3D-CAD models and hardware prototypes
22. Others
    • Free topic. We also welcome self motivated applicants who can take the lead of their own research project.

Internship Location

PFN Tokyo Office

Otemachi Bldg., 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004

Key Qualifications

• PFN is seeking highly motivated and skillful individuals who can develop applications, tools, etc. independently.
• If you have knowledge or development experience in the themes that are stated in【Theme List】section, we’ll take them into consideration, but it’s not required. The minimum requirements are:
  • Currently enrolled in high school, technical college, university, or graduate school. Negotiable for those attending other higher education institutions
  • Fluent in Japanese or English
  • Strong communication skills
  • Prior experience in programming (any language)
  • Can work from our Tokyo office on weekdays
    (we’re not accepting remote working for the internship program currently)
• Do not hesitate to apply even if you don’t have prior experience in full-scale development.

Important Notes before You Apply

• We are not accepting applications from students who need support for obtaining the designated activity visa to work as an intern. Call for such applications for this year has already been closed.
• Non-Japanese students who are studying at Japanese universities with student visa must make sure to get 【Permission for Other Activity / 資格外活動許可】before the start date of your PFN internship program
• You need to let us know in advance if any administrative work is required to receive academic credit from your school. Please note that depending on the complexity of the work, PFN may not be able to accommodate your request.

How to Apply

• Click this application form to apply. To access the application form, you will need to log in with a Google account.  We use your personal information filled in the application form for selection of summer interns.

• About the submission of your portfolio
  • Summarize your skills and qualifications freely in an A4 paper to pitch yourself.
  • Highlight and showcase some of your best work such as software you have developed, published papers, awards or prizes you have received, programming contests you have participated in, your blog, your sites, Twitter account, and other social media sites.
  • Please upload it using the “Portfolio to pitch yourself” field in the application form.
• Deadline：April 18th (Thu) 12:00 PM JST (No late submission allowed)
• For inquiries：email to intern2019-admin@preferred.jp

Selection Process

• First screening
  • After we close the application on April 18, we will send the coding tests to all applicants (in principle) on April 19.
    【Updated on Mar 29】For those who choose 21. Product Design, we will send you the guideline of a task instead of coding test.
  • The deadline for completing these tests is May 7 (subject to change).
• Interview
  • The interview will take place within 3 weeks from May 27.
  • For students living in distant areas, PFN will arrange an online (video) interview via Wepow.
• Offer
  • The letter of acceptance is planned to be delivered on June 25 or later

Basic Employment Conditions And Benefits

• Salaries：
  • 2,500 yen an hour for technical college, university, graduate school students
  • 2,000 yen an hour for high school students
• Work hours：8 hours in principle. 5 days a week excluding Saturdays, Sundays, and public holidays.  
• Commuting fee support：PFN will pay for your daily commute to and from the office in an approved route.
• Travel cost：For students traveling a long distance by plane or Shinkansen to participate in the internship, PFN will support the cost for one round trip to relocate between the Tokyo area and the place where you’re currently living.
• Accommodation support：For students coming from distant areas, PFN will provide an accommodation allowance of 5,000 yen per day for the entire period of your internship including holidays.
  • You need to arrange a place to stay by yourself. Reasonable weekly rental apartments are available near PFN office ranging from 100,000 to 150,000 yen a month.
  • Please note that the accommodation allowance is taxable.

投稿 Call for Applications for PFN Summer Internship 2019 in Tokyo は Preferred Networks, Inc. に最初に表示されました。

Official application for the 2019 summer internship program has been closed, we look forward to your application for the 2020 summer internship in this September!

Preferred Networks (PFN) will be organizing internship programs 2019 summer in Tokyo. In order to make the process smooth for students from outside of Japan, we have already opened an early bird application for them.

We are a growing startup with about 190 members based in Tokyo, Japan, focusing on applying deep learning to industrial problems such as autonomous driving, manufacturing, and bio-healthcare. Recently we revealed our plan towards personal robots. We are also actively developing the deep learning framework Chainer.

We look for brilliant students who have expertise on various topics, such as deep learning, robotics, computer vision, bioinformatics, human computer interaction, distributed computing, simulation, etc.

In previous years, by selecting highly capable interns and encouraging them to tackle challenging and important problems, some of the internship results have been published at top conferences such as ICML, ICLR or workshops at ICRA, ICCV, and NIPS.

During the internship, you will have a unique opportunity to collaborate with highly motivated experts for working on real-world applications of deep learning, while staying in Tokyo, one of the most attractive cities in the world.

We are looking forward to receiving your applications, following the instructions below.

REQUIREMENTS

• Target of this program:

  • Students outside of Japan

• Application information:

  • Resume / CV (PDF format only. Please DO NOT include any personal or private information [e.g., age, race, nationality, religion, personal address, phone number] except name, email address, affiliation)
  • Github account (optional)

• How to apply:

  • Please fill the google forms (official application for 2019 summer internship program has been closed, we look forward to your application for 2020 summer internship in this September!)and submit your application ※Please DO NOT submit your application via workable application form (or LinkedIn etc.) but the google form only!
  • Due Date: January 7th Monday, 12:00 PM JST
  • No later application that said due date above will be accepted (we are planning to open, another call for students in Japan by April)
  • The review process takes about 2-3 weeks after submission
  • Usually, getting a visa for working in Japan takes up to 3 months

• Interview process (expected time to complete: 6-8 weeks):

  • Document review(※You’ll receive the result during the 1st~2nd week due to the year-end holiday in Japan)
  • One-way recorded interview
  • Live interview with our engineers / researchers including coding test

• Requirements:

  • Experience in at least one of the technology areas (listed in the application form) other than lectures
    e.g., published a paper, won a competition, part-time work, open source contribution
  • Basic knowledge and skills of the technical field you are applying to
  • Formally enrolled in university or research institute outside of Japan, or have recently graduated
  • Fluent in English or Japanese
  • Able to work full-time on weekdays at our Tokyo office during the period

• Preferred experience & skills:

  • Strong programming skill (any programming language)
  • Experience with Numpy / scipy / deep learning frameworks / ROS
  • Experience with software, service, or machine development
  • Experience working with shared codebases (e.g. GitHub / bitbucket / etc)
  • Contribution to open source projects

• Work time & location:

  • 8 hours/day, 5 days/week (excluding national holidays)
  • Location: Center of Tokyo
    Preferred Networks Tokyo office: Otemachi Bldg. 3F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
    https://www.preferred.jp/en/company
  • Contact: intern2019-admin@preferred.jp

• Affiliations of past interns (partial list):

  • Université de Montreal
  • University of Waterloo
  • University of Texas at Austin
  • University of Texas at Dallas
  • University of Southern California
  • University of Michigan
  • Sharif University of Technology
  • Singapore University of Technology and Design
  • Rutgers University
  • Indian Institute of Technology
  • Imperial College London
  • Chinese Academy of Sciences
  • Brown University

BENEFITS

• The period of the internship can be flexibly arranged from a list of beginning/ending dates
• We will cover round-trip flight cost
• Residential and living expense support will be provided
• Interns are paid a competitive salary
• We require a minimum of twelve weeks (60 business days), in order to be able to tackle a challenging task

For Applying:

Official application for the 2019 summer internship program has been closed, we look forward to your application for 2020 summer internship in this September!

投稿 Summer Internship 2019 for students from outside of Japan は Preferred Networks, Inc. に最初に表示されました。

Important notice:

Note that this program is only for students who already have visa eligibility to work as an intern this summer in Japan. We are not accepting applications from students who need support for obtaining the designated activity visa to work as an intern because the due date for processing such applications has already passed.

Guidelines for applicants

● Characteristics of PFN Internship

• Over the two-month period, PFN engineers will be assigned to work with each of you as a mentor. You will have opportunities to discuss and study your theme with specialists in various fields including deep learning, computer vision, natural language processing, robotics, bio-healthcare, reinforcement learning, and distributed processing.
• After the internship, you can make public your research result by writing a paper or making it OSS, to the extent possible.  

● Period

Start date：Between late July and early August depending on your schedule

End date：Friday, Sept.21, 2018

※You can choose to continue to work in the week of Sept. 24-28 under the same terms and conditions.

Note that this year’s internship will end on Sept. 21 in consideration of the fact that many schools start their fall semester in late September. If you need more time to finalize your research or want to spend more time with our staff, you can continue to work until the end of September under the same terms and conditions. We understand you may have school or family commitments during the internship which might range from lab activities to attending academic conferences, to returning home. We are very flexible about your need to take days of absence due to these reasons.

● Key Qualifications

PFN is seeking highly motivated and skillful individuals who can develop applications, tools, etc. on your own. Having knowledge or development experience in the themes listed below is a plus but not a must. Minimum requirements are:

• Currently enrolled in high school, technical college, university, or graduate school. Negotiable for those attending other higher educational institution
• Fluency in Japanese or English
• Strong communication skills
• Prior experience in programming (any language)
• Willingness to come to work in our Tokyo office on weekdays

Do not hesitate to apply even if you don’t have prior experience in full-scale development.

【Important notes before you apply】

• We are not accepting applications from students who need support for obtaining the designated activity visa to work as an intern because the due date for processing such applications has already passed.
• You need to let us know in advance for any administrative work required for receiving academic credit from your school. Please note that depending on the complexity of the work, PFN may not be able to accommodate your request.

● Place of work

PFN Tokyo Office

Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004

● Basic working conditions and benefits

• Salaries：2,500JPY an hour for a technical college, university, graduate school students. 2,000JPY an hour for high school students
• Work hours：Eight work hours in principle. Five days a week excluding Saturdays, Sundays, public holidays.  
• Commuting fee support：PFN will pay for your daily commute to and from office in an approved route.
• Travel cost：For students traveling a long distance by plain or Shinkansen bullet train to participate in the internship, PFN will support a round trip to relocate to the Tokyo area.
• Accommodation support：For students coming from distant parts who would take roughly 60 minutes or longer to commute, PFN will provide a housing allowance of 5,000JPY a day covering the entire period of your internship. You need to arrange a place to stay by yourself. Reasonable weekly rental apartments are available near PFN office ranging from 100,000 to 150,000 JPY a month. Please note that the accommodation allowance is taxable.

● How to Apply

Go to： Application form

※ Click the above form to apply. To access the application form, you will need to log in with a Google account.

Deadline：By 23:59 Monday, April 30, 2018, Japan time

For inquiries：Send us an email at intern2018@preferred.jp

※About your portfolio

Summarize your skills and qualifications freely in a A4 paper to pitch yourself and highlight and showcase some of your best work such as software you have developed, a list of published papers, awards or prizes you have received, programming contests you have participated in, your blog, twitter account, and other social media sites.

● Themes

Let us know which of the following areas of study you would like to work on during the internship. We will decide your theme after speaking with you. You must choose your 1st and 2nd preferences in the application form. If you have more than two areas of interest, select the 3rd preference, which is optional.

1. Theoretical study of Machine learning/deep learning
2. Computer vision
3. Deep reinforcement learning
4. Robotics
5. Bio-healthcare
6. HPC and distributed data management for distributed deep learning/deep learning
7. Natural language processing
8. Speech processing
9. VR／AR
10. Human computer interaction, human machine interaction
11. Applications of deep learning to animation, creator support
12. Development of Chainer
13. Development of area-specific libraries on Chainer
14. R&D of machine learning algorithms such as anomaly detection
15. Information visualization tool and front-end development for machine learning
16. Machine learning research support, cluster management, experiment management system development
17. Development of dedicated accelerator/processor for deep learning
18. Development of compiler/optimizer for deep learning
19. Development of IoT/Edge Heavy Computing platform
20. Other
21. （New) R&D of automatic tuning methods for deep learning
22. （New) Video analytics (sports, etc…)

● Selection process

▼First screening

After sending the application by April 30, you will receive two tests: (1) Online self-interview (recording) (2) Coding test. Deadline for completing these tests is  May 14 (subject to change).

▼Interview

An interview will be scheduled sometime during the two weeks starting from June 5. For students living in distant areas, PFN will arrange a video chat such as Skype.

▼Letter of acceptance (by late June)

投稿 Call for applications for PFN summer internship 2018 は Preferred Networks, Inc. に最初に表示されました。

We are a growing startup with about 120 members based in Tokyo, Japan, focusing on applying deep learning to industrial problems such as autonomous driving, manufacturing, and bio-healthcare. We are actively developing the deep learning framework Chainer.

We are looking for brilliant students who have expertise in various topics, such as deep learning, reinforcement learning, computer vision, bioinformatics, natural language processing, distributed computing, simulation, etc.

In previous years, by selecting highly capable interns and encouraging them to tackle challenging and important problems, some of our interns were able to have their work published at top conferences such as ICML, ICCV,  ICRA, and ICLR.

This year, we would like to expand our reach to attract talented students around the world and collaborate with them to tackle challenging problems in AI for a longer period, by introducing this AI residency program.

During the residency program, you will have a unique opportunity to collaborate with our excellent research team members at PFN and work on real-world applications of deep learning, while living in Tokyo; one of the most attractive cities in the world.

We are looking forward to receiving your applications! Please see below for the instructions.

●Target of this program

• Students outside of Japan

●Work time & Location:

• Business hours:
  8 hours/day, 5 days/week (excluding national holidays)
• Location: Center of Tokyo
  Preferred Networks Tokyo office: Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
  https://www.preferred.jp/en/about

●Period & Compensation:

• The period of the residency program can be flexibly arranged between September 2018 and August 2019, the minimum term is 6 months
• AI residency program students are paid a competitive salary
• We will cover residence and travel cost

●Requirements:

• We can only accept Ph.D. students or new graduates who have been accepted for Ph.D. program starting 2018 Fall
  • Applicants are responsible for negotiating with their university for one year leave or deferring the admission to next year
• Experience in at least one of the technology areas (listed below) other than only attending lectures
  e.g., published a paper, won a competition, part-time work, open source contribution
• Strong programming skill (any programming language)
• Fluent in either English or Japanese
• Able to work fulltime on weekdays at our Tokyo office during the period

●Preferred experience & skills:

• Machine learning and deep learning
• Experience with NumPy / SciPy / deep learning frameworks
• Experience with software & service development
• Experience working with shared codebases (e.g. GitHub / bitbucket / etc)
• Contribution to open source projects

●Candidate themes (subject to change)

   1. Technology areas: Sub-field of machine learning, such as

a. Deep learning theory

b. Reinforcement learning

c. Computer vision

d. Natural language processing

e. Parallel / distributed computing

   2. Application areas: Advanced applications, such as

a. Object detection / tracking / segmentation from image / video

b. Robotics / factory automation / predictive maintenance

c. Life science / healthcare / medicine

d. Human machine interaction

e. Design / content creation / visualization

f. Deep learning software (Chainer, CuPy, ChainerMN/CV/RL, etc)

g. Optimization for deep learning hardware

●Application information:

• Resume / CV (PDF format only. Please DO NOT include any personal or private information [e.g., age, race, nationality, religion, personal address, phone number] except name, email address, affiliation)
• Github account (optional)

●How to apply:

• Please fill the google forms and submit

• Due: March 20th, 11:59 pm Tuesday (PDT)

• No late submission will be accepted
• The review process takes about 6-8 weeks after submission
• Usually, getting a visa for working in Japan takes up to 3 months

●Interview process:

1. Document review
2. One-way video interview (webcam, recording)
3. Skype interview in English or Japanese (multiple times if necessary)

If you have questions, please contact us at hr-pfn@preferred.jp (Sorry but no late application is accepted for fairness)

投稿 Call for applications for PFN AI residency program 2018-2019 in Tokyo は Preferred Networks, Inc. に最初に表示されました。

We are a growing startup with about 110 members based in Tokyo, Japan, focusing on applying deep learning to industrial problems such as autonomous driving, manufacturing, and bio-healthcare. We are actively developing the deep learning framework Chainer.

We look for brilliant students who have expertise on various topics, such as deep learning, reinforcement learning, computer vision, bioinformatics, natural language processing, distributed computing, simulation, etc.

In previous years, by selecting highly capable interns and encouraging them to tackle challenging and important problems, some of the internship results have been published at top conferences such as ICML or workshops at ICRA and ICCV.

During the internship, you will have unique opportunity to collaborate with highly motivated experts for working on real-world applications of deep learning, while staying in Tokyo, one of the most attractive cities in the world.

We are looking forward to receiving your applications, following the instructions below.

● Target of this program:

• Intern Students outside of Japan

● Work time & Location:

• Business hours:
  8 hours/day, 5 days/week (excluding national holidays)
• Location: Center of Tokyo
  Preferred Networks Tokyo office: Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
  https://www.preferred.jp/en/about

● Period & Compensation:

• The period of the internship can be flexibly arranged
  • You can choose the beginning date as any Wednesday from May 8th or later, and the final date from July 27th, August 16th, or September 21st
• We require a minimum of eight weeks (40 business days), in order to be able to tackle a challenging task
• Interns are paid a competitive salary
• We will cover residence and travel cost

● Requirements:

• Experience in at least one of the technology areas (listed below) other than lectures
  e.g., published a paper, won a competition, part-time work, open source contribution
• Strong programming skill (any programming language)
• Formally enrolled in university or research institute outside of Japan during 2018-2019 school year
• Fluent in either English or Japanese
• Able to work fulltime on weekdays at our Tokyo office during the period

● Preferred experience & skills:

• Machine learning and deep learning
• Experience with numpy / scipy / deep learning frameworks
• Experience with software & service development
• Experience working with shared codebases (e.g. github / bitbucket / etc)
• Contribution to open source projects

● Candidate themes (subject to change)

1. Technology areas: Sub-field of machine learning, such as

a. Deep learning theory

b. Reinforcement learning

c. Computer vision

d. Natural language processing

e. Parallel / distributed computing

2. Application areas: Advanced applications, such as

a. Object detection / tracking / segmentation from image / video

b. Robotics / factory automation / predictive maintenance

c. Life science / healthcare / medicine

d. Human machine interaction

e. Design / content creation / visualization

f. Deep learning software (Chainer, CuPy, ChainerMN/CV/RL, etc)

g. Optimization for deep learning hardware

● Application information:

• Resume / CV (PDF format only. Please DO NOT include any personal or private information [e.g., age, race, nationality, religion, personal address, phone number] except name, email address, affiliation)
• Github account (optional)

● How to apply:

• Please fill the google forms and submit

• Due: January 14th 11:59 pm Sunday (PST)

• No late submission will be accepted
• The review process takes about 6-8 weeks after submission
• Usually, getting a visa for working in Japan takes up to 3 months

● Interview process:

1. Document review
2. One-way video interview (webcam, recording)
3. Skype interview in English or Japanese (multiple times if necessary)

If you have questions, please contact us at hr-pfn@preferred.jp (Sorry but no late application is accepted for fairness)

投稿 Second call for application for PFN summer internship 2018 in Tokyo は Preferred Networks, Inc. に最初に表示されました。

We are a growing startup with about 100 members based in Tokyo, Japan, focusing on applying deep learning to industrial problems such as autonomous driving, manufacturing, and bio-healthcare. We are actively developing the deep learning framework Chainer.

We look for brilliant students who have expertise on various topics, such as deep learning, reinforcement learning, computer vision, bioinformatics, natural language processing, distributed computing, simulation, etc.

In previous years, by selecting highly capable interns and encouraging them to tackle challenging and important problems, some of the internship results have been published at top conferences such as ICML or workshops at ICRA and ICCV.

During the internship, you will have unique opportunity to collaborate with highly motivated experts for working on real-world applications of deep learning, while staying in Tokyo, one of the most attractive cities in the world.

We are looking forward to receiving your applications, following the instructions below.

● Target of this program

• Students outside of Japan

● Work time & Location:

• Business hours:
  8 hours/day, 5 days/week (excluding national holidays)
• Location: Center of Tokyo
  Preferred Networks Tokyo office: Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
  https://www.preferred.jp/en/about

● Period & Compensation:

• The period of the internship can be flexibly arranged though it usually starts in June and finish by the end of August
• We require minimum of two months (40 business days), in order to be able to tackle a challenging task
• Interns are paid a competitive salary
• We will cover residence and travel cost

● Requirements:

• Experience in at least one of the technology areas (listed below) other than lectures
  e.g., published a paper, won a competition, part-time work, open source contribution
• Strong programming skill (any programming language)
• Formally enrolled in university or research institute outside of Japan during 2018-2019 school year
• Fluent in either English or Japanese
• Able to work fulltime on weekdays at our Tokyo office during the period

● Preferred experience & skills:

• Machine learning and deep learning
• Experience with numpy / scipy / deep learning frameworks
• Experience with software & service development
• Experience working with shared codebases (e.g. github / bitbucket / etc)
• Contribution to open source projects

● Candidate themes (subject to change)

1. Technology areas: Sub-field of machine learning, such as

     a. Deep learning theory

     b. Reinforcement learning

     c. Computer vision

     d. Natural language processing

     e. Parallel / distributed computing

2. Application areas: Advanced applications, such as

     a. Object detection / tracking / segmentation from image / video

     b. Robotics / factory automation / predictive maintenance

     c. Life science / healthcare / medicine

     d. Human machine interaction

     e. Design / content creation / visualization

     f. Deep learning software (Chainer, CuPy, ChainerMN/CV/RL, etc)

     g. Optimization for deep learning hardware

● Application information:

• Resume / CV (PDF format only. Please DO NOT include any personal or private information [e.g., age, race, nationality, religion, personal address, phone number] except name, email address, affiliation)
• Github account (optional)

● How to apply:

• Please fill the google forms and submit

• Due: September 29th, 11:59 pm Friday (PST)

• No late submission will be accepted (we are planning to open the 2nd call for application by January, and another call for students in Japan by May)
• The review process takes about 6-8 weeks after submission
• Usually, getting a visa for working in Japan takes up to 3 months

● Interview process:

1. Document review
2. One-way video interview (webcam, recording)
3. Skype interview in English or Japanese (multiple times if necessary)

If you have questions, please contact us at hr-pfn@preferred.jp (Sorry but no late application is accepted for fairness)

投稿 Call for application for PFN summer internship 2018 in Tokyo は Preferred Networks, Inc. に最初に表示されました。

Application Guideline

●Period

August 1st – September 30th 2017
(Negotiable.)

●Time & Place

8 hours/day, 5 days/week (excluding holidays)
Otemachi-Bldg. 2F 1-6-1, Otemachi, Chiyoda-ku,Tokyo, 100-1004

●Salary

• High school: 1500Yen/hour
• Technical college/Undergraduate/Graduate: 1800Yen/hour
• Transportation expenses (up to 10000Yen/month) are also covered.

●Why join the PFN internship program?

• You will be collaborating and be mentored by experts in various fields including deep learning, computer vision, natural language processing, reinforcement learning, algorithms, distributed processing, etc.
• You can make public the results of your work during the internship program, as OSS or a paper, etc. (Some restrictions might apply.)

●Qualification requirements

We are looking for highly motivated people who have development capabilities. Expertise in the fields mentioned below, or prior development experience are taken into consideration, but are not a must. Application requirements are as follows:

• Currently students (High school, technical college, college, graduate students, others could also be discussed.)
• Able to communicate in English or Japanese
• Able to communicate on one’s own initiative
• Have programming skills (regardless of the programming language)
• Able to work fulltime on weekdays at our Tokyo office

# We will prepare accomodation for those who live far from Tokyo.
# You can still apply even if you are not a fully-fledged application developer.

●How to apply

Please submit the application form below.
https://docs.google.com/forms/d/e/1FAIpQLSevjHAtBhq9380kzDLXQ1dySoWa_p7N_VhgTHZnC4pcJa75hw/viewform

Questions about the internship program are also accepted by intern2017@preferred.jp.

Application form note

Proof of skills; upload your document following the steps below that explains your strengths and expertise fields, etc. (Microsoft Word or Google docs, one A4 page)
E.g., List of papers, received awards, developed/used Software&Services, programming contests participation history, personal website/blog, twitter account, etc.
https://www.preferred.jp/wp-content/uploads/2017/03/intern2017_GoogleUpload_3.pdf

Themes you want to do; please include your interest in the selected themes and your expectations from the internship using less than 400 characters.

# This is a very important for both the admission process, and the internship theme selection.

●Application Deadline

May. 7th, 2017 23:59 (JST)

●Selection process

Documents screening
# Takes around one weeks before result is returned.

Pre-interview task screening
# The task will be announced to those who passed the above.

Interview (generally once)
# Skype interview for remote applicants

Acceptance notice (Late June)

●Themes

[Machine Learning / Mathematics Fields]

Applications

• Chainer development
• Image recognition
• Video analysis
• Content generation (Generation of images, videos, sounds, etc.)
• Natural language processing
• Speech recognition
• Anomaly detection
• IoT
• Data compression
• Robotics (Robot arms, bipedal walking, self-driving cars, path planning)
• Genomics, Epigenomics, proteomics
• Deep Learning on embedded systems
• LSI design optimization

Research

• Distributed algorithm, Distributed deep learning
• Reinforcement learning
• Optimization
• Deep generative models
• Model compression
• Neural network quantization
• Machine learning with limited labels (One-shot learning, Weakly supervised learning, Semi-supervised learning, Meta learning)
• Machine learning using simulators
• Interpretability in machine learning
• Differential privacy
• Communication or collaboration emergence

[Front-end or Back-end Development]

• Chainer development
• SensorBee
• PaintsChainer
• Stream processing
• Tools development
• Web development
• Networking
• High-performance computing
• 3DCG
• Unity development
• AR or VR

[Chip Development]

• FPGA design

投稿 PFN 2017 Summer Internship Program は Preferred Networks, Inc. に最初に表示されました。

Target of this program

– Students outside of Japan

Work time & Location:

– Business hours:
8 hours/day, 5 days/week (excluding national holidays)
– Location: Center of Tokyo
Preferred Networks Tokyo office: Otemachi Bldg. 2F, 1-6-1, Otemachi, Chiyoda-ku, Tokyo, Japan 100-0004
https://www.preferred.jp/en/about

Period & Compensation:

– The period of the internship can be flexibly arranged though it is usually held during July & August
– We require minimum of two month (40 business days), in order to be able to tackle a challenging task
– Interns are paid a competitive salary
– Residence and travel cost is to be provided

Requirements:

– Formally enrolled in university or research institute outside of Japan during 2017-2018 school year
– Fluent in English (or Japanese)
– Good programming skill (any programming language)
– Computer science basics
– Able to work full-time on weekdays at our Tokyo office during the period

Preferred experience & skills:

– Machine learning and deep learning basics
– Experience with software & service development
– Experience with team development
– Contribution to open source projects

Candide themes (subject to change)

1. Technology areas: Sub-field of machine learning, such as
Deep learning theory
Reinforcement learning
Computer vision
Parallel distributed learning
Weakly supervised learning
Transfer learning
Anomaly detection
Deep Generative Model
Others
2. Application areas: Advanced IoT applications, such as
Image recognition
Robotics & machine control
Life science & medicine
Machine Learning Framework development (Incl. OSS such as Chainer)
Others

(FYI) Projects of 2016 summer interns
DQN with Differentiable Memory Architectures
Multi-modal Deep Generative Model for Anomaly Detection
CNN based robotic grasping for randomly placed objects by human demonstration
Anomaly Detection by ADGM / LVAE
Imitation Learning for Autonomous Driving in TORCS
3D Volumetric Data Generation with Generative Adversarial Networks
Bayesian Dark Knowledge and Matrix Factorization
Automatically Fusing Functions on CuPy
Generation of 3D-avatar animation from latent representations
Response Summarizer: An Automatic Summarization System of Call Center Conversation
Product marketing in conversations

Application information:

– Resume / CV (PDF format only. Please DO NOT include any private information e.g. age, personal address, phone number, etc.)
– Name, e-mail address, affiliation
– Github account (optional)
– Linked.in account (optional)

How to apply:

– Please fill the google form. (Application is now closed and no e-mail application will be accepted)
– Due: January 20th, 11:59pm Friday (PST)
– No late submission will be accepted
– The interview process takes about 2-4 weeks after application submission
– Usually, getting visa support in Japan takes up to 3 months so that the preparation must be done in advance of the internship period

Interview process:

1. Document review
2. Skype interviews in English or Japanese (multiple times if necessary)

If you have questions, please contact us at hr-pfn@preferred.jp (Sorry but no late application is accepted for fairness)

投稿 [Closed] 2nd Call for application: 2017 summer internship in Tokyo は Preferred Networks, Inc. に最初に表示されました。