NEWS

PFN 2017 Summer Internship Program

As goes the tradition, Preferred Networks (PFN) will be organizing the internship program this summer too. From this year, we are also looking for front-end/back-end and chip development in addition to machine learning. We welcome applications not only from machine learning field but also from many people. We are looking forward to receiving students who want to join us in creating new technologies and services. Students who previously applied are also welcome to try again this year.
(Application from overseas with a need for VISA is already closed for this year)

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-networks.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

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