Event & Talk

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

PFN members gave a tutorial on deep learning implementations at AAAI-17

[San Francisco, February 5th] Preferred Networks members, Seiya Tokui and Kenta Oono, gave a tutorial titled “”Deep Learning Implementations and Frameworks (DLIF)” at an international conference (AAAI-17).

Based on the fact that using software frameworks is fundamental in deep learning applications, the purpose of this tutorial is to help users to select an appropriate deep learning framework by describing the basics of implementation, design choices, and comparison of the features of the existing frameworks including Chainer,

The presentation slides and sample code can be found here.

AAAI has more than 30 years history as a prestigious academic conference in artificial intelligence. It hosted 24 tutorials this year, with diverse topics from machine learning theory to AI applications to IoT or robotics. The DLIF tutorial attracted the largest number of pre-registrants out of them. This work was co-organized by Dr. Atsunori Kanemura of AIST (National Institute of Advanced Industrial Science and Technology in Japan), also under supervision from Dr. Toshihiro Kamishima and Dr. Hideki Asoh.




(From right to left, Seiya Tokui of PFN, Kenta Oono of PFN and Dr.Atsunori Kanemura of AIST )

Preferred Networks will continue contributing to academia through open source software, research papers, and tutorial talks.

[Closed] 2nd Call for application: 2017 summer internship in Tokyo

Preferred Networks will be organizing internship programs next summer in Tokyo. In order to make the process smooth for the students outside of Japan, we open an early bird application opportunity for the first time. We are looking forward to welcoming students who want to join us in creating new technologies and services. Note that similar programs will follow for the students both in & outside of Japan, also after this first call.

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-networks.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)

[Closed] Call for application: 2017 summer internship in Tokyo, Japan

Note: this application is now closed. We are planning to have one more turn early January.

Preferred Networks will be organizing internship programs next summer in Tokyo. In order to make the process smooth for the students outside of Japan, we open an early bird application opportunity for the first time. We are looking forward to welcoming students who want to join us in creating new technologies and services. Note that similar programs will follow for the students both in & outside of Japan, also after this first call.

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-networks.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 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 documents:

– Resume (CV)


How to apply:

– Application has been closed
Due: November 23rd, 11:59pm (PST)
– 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)

Oct22 PFN Career information event

On October 22 (Thursday) Preferred Networks will hold a career information event with talks from the PFN founders Nishikawa and Okanohara, as well as researchers and engineers working at PFN. There will also be a Q&A session where you can get in touch with many of our employees.

Please note: This time, all the talks will be in Japanese, but we are planning to have another event aimed at an English-speaking audience soon. Follow us on Twitter (https://twitter.com/PreferredNet) to stay updated!

Time/Date: Oct 22, 2015, 18:30-20:30 (doors open at 18:00)
Location: エムワイ貸会議室 Ochanomizu
Address: Ochanomizu Union Building (4th floor), 2-1-20 Kandasurugadai, Chiyoda-ku, Tokyo 101-0062

PFN is quickly expanding and we are always looking for great talent. Please visit our job page for more information: https://www.preferred-networks.jp/job_en

Sep7-11 Tobias Pfeiffer Speaks at MobiCom Panel@Paris

Tobias Pfeiffer, Software Engineer at Preferred Networks, participated in a panel at the 21st International Conference on Mobile Computing and Networking (MobiCom), one of the top conferences in the field of mobile computing and wireless networking, that was held from September 7-11 in Paris (France).

In the panel called “Big Data, IoT, … Buzz Words For Academia Or Reality For Industry?”
which was chaired by Wenjun Hu (Yale University, USA), Rui Aguiar (Universidade de Aveiro, Portugal), and Hiroshi Esaki (University of Tokyo, Japan), he presented Preferred Networks’ approach to the Internet of Things, based on Edge-Heavy Computing and Deep Learning. Afterwards, the panelists discussed with the audience the state and future development of the IoT, for example questions such as: “How can academic institutions obtain real-world data for their research?” or “What are important developments that need to happen to advance the current state of IoT?”

PFN is at Cisco Live! at San Diego

Preferred Networks, Inc. (PFN) is pleased to announce its participation in Cisco Live! 2015 in San Diego, California, June 8th-11th.

 

 

 

PFN is providing a live demo of surveillance video analytics based on its product, Deep Intelligence™ in Motion (DIMo) v1.0, which is designed to realize network-wide intelligence for IoT. In addition, PFN is demonstrating its latest results using deep learning for autonomous optimization of machine behaviors. A new demo video is being shown for the first time that shows how virtual race cars learn to control themselves using deep reinforcement learning.

 

 

In addition to developing video analytics products, PFN is also focusing on research and development for revolutionizing industrial IoT areas including manufacturing and smart transportation/cities. These new methods use cutting-edge deep learning technologies to combine information extracted from multiple types of sensors. PFN’s demonstration is located at booth #3131 in the World of Solutions hall, as part the booth for Cisco Entrepreneur in Residence, the incubation program in which PFN is participating. Please come to our booth and enjoy our new technologies.

 

 

October 2: CSO Junichi Hasegawa to speak at OECD’s Global Forum