2024 Summer Internship
List of Courses and Themes
March 27, 2024

List of Courses
Please refer to the recruitment instructions on our website for more details on thewhole internship.
Course4-weeks Group Dev.

10-days Short Dev.

7-weeks R&D

PeriodAug. 26th (Mon.)- Sep. 20th (Fri.)

Aug. 19th (Mon.)- Aug. 30th (Fri.)

Aug. 8th (Thu.)- Sep. 27th (Fri.)

Sep. 6th (Fri.)- Sep. 20th (Fri.)
Must participate for the entire duration inprinciple
Theme
Openings

2 themes
GD01〜GD02

4 themes
JE01〜JE04

Temporary absence
allowed46 themes
RD01〜RD46

There are two categories in the 7-week R&D course according to its orientation.
Please use this as a reference when choosing a theme.
Project InternshipResearch Internship

An internship that engages in the tasksof projects that aim for practicalproblem solving, and conducts
research and development of coretechnologies and the development of
products and services

An internship that engages inpioneering and challenging research
activities with the aim of submitting
papers to top international conferences

The course and theme of your choice will be selected when you submit thescreening task that we will guide you through after your application. The actual
theme and the job content that you will work on will be decided in consultation with
our team members during the selection process.

1

List of Themes* IDs are non-continuous because the themes held only in Japanese are omitted from the list. If you
are interested in these themes, please see the list of themes in Japanese.
ID

Theme

Area

CourseCategory

GD01[4-weeks Group Development] App/Service
Development

Company Wide

4-weeks Group Dev.

-

GD02

[4-weeks Group Development] Data Science
Engineering

Company Wide

4-weeks Group Dev.

-

JE04

[10-Days] Employment Experience in the Development
and Operation of a web system for Matlantis, ageneral-purpose atomic-level simulator

Material Science /Matlantis

10-days Short Dev.

-

RD01

Machine learning research for foundation model

Foundation Model

7-weeks R&D

Research

RD02

Building a Video-Based World Model

Foundation Model

7-weeks R&D

Research

RD03

Automating Dataset Generation for LLM Pretraining

Foundation Model /Language

7-weeks R&D

Project

RD04

Acceleration of pretraining for large language model

Foundation Model /Language

7-weeks R&D

Project

RD05

Alignment of large language model

Foundation Model /Language

7-weeks R&D

Project

RD06

Development of text embedding models

Foundation Model /Language

7-weeks R&D

Project

RD07

Lightweight inference of large language models

Foundation Model /Language

7-weeks R&D

Project

RD08

Implementation of a task in the field of computer
vision to a vision foundation model

Foundation Model /Vision7-weeks R&D

Project

RD10Development on Measurement and Analysis for theComputing Infrastructure

ComputingInfrastructure / Cluster

7-weeks R&D

Project

RD11

Development of MN-Core Compiler

ComputingInfrastructure / MN-Core

7-weeks R&D

Project

RD12Development ML/HPC Application on MN-Core

ComputingInfrastructure / MN-Core

7-weeks R&D

Project

RD13

Development of framework and library for deploying
deep learning models in real world

ComputingInfrastructure /Ecosystem

7-weeks R&D

Project

RD14

Development of CuPy

ComputingInfrastructure / Cupy

7-weeks R&D

Project

RD15

AutoML for LLM

ComputingInfrastructure / AutoML

7-weeks R&D

Project

RD16

Development of Optuna and Optuna Dashboard

ComputingInfrastructure / Optuna

7-weeks R&D

Project

RD17

Applied research and development of machinelearning and atomic simulation on materials

Material Science

7-weeks R&D

Research

RD18

Implementation of material discovery algorithms in
Matlantis

Material Science /Matlantis

7-weeks R&D

Research

RD19

Development and Operation of a web system for
Matlantis, a general-purpose atomic-level simulator

Material Science /Matlantis

7-weeks R&D

Project2

RD20

Enhance monitoring and improve the PFP API in
Matlantis, a general-purpose atomic-level simulator.

Material Science /Matlantis

7-weeks R&D

Project

RD21

Research on Neural Network Potential(NNP) in Drug
Discovery

Drug Discovery

7-weeks R&D

Research

RD22

Research on machine learning models of proteins andbiomolecules for drug discovery

Drug Discovery

7-weeks R&D

Research

RD23

Improving drug-likeness in machine learning based
Drug Design

Drug Discovery

7-weeks R&D

Research

RD25

High-Dimensional Tabular Data Generation

Health Care

7-weeks R&D

Project

RD26

Research on machine learning methods for medical
imaging

Health Care

7-weeks R&D

Research

RD27

Reconstruction, editing, and generation of 3D models
and free-viewpoint videos

Entertainment / PFN
3D/4D Scan

7-weeks R&D

Project

RD28

Reconstruction, editing, and generation of 3D models
and free-viewpoint videos

Entertainment / PFN
3D/4D Scan

7-weeks R&D

Research

RD30

Application of Generative AI Technologies to Creativity

Entertainment / Creative

7-weeks R&D

Research

RD39

Research and Development of Advanced
Segmentation Methods for SAR Images

Satellite Image

7-weeks R&D

Project

RD40

Research and development on theestimation/prediction of various weather conditions
using the advanced high-spatio-temporal resolution
3D weather data

Meteorology

7-weeks R&D

Project

RD41

Beyond BackPropagation : Attack on the Current SGD
based algorithms

Deep Learning

7-weeks R&D

Research

RD42

Beyond Group Equivariant Framework: Extending
Neural Fourier Scheme to non-group structure

Deep Learning

7-weeks R&D

Research

RD43

Optimizing the latent space for Flow based algorithms

Deep Learning

7-weeks R&D

Research

RD44

Extended Reality (VR, MR. AR) x AI

HCI

7-weeks R&D

Research

RD45

Interactive Systems in HCI for Generative AI

HCI

7-weeks R&D

Research3

GD01[4-weeks Group Development] App/Service
Development
Area: Company Wide
Course: 4-weeks Group Development Course
We are seeking individuals who have experience in application development,
regardless of their research and development experience in machine learning andgenerative AI. The selected candidates will work in groups of approximately 3-4
people, collaborating with a mentor who is a PFN employee, to plan and develop
services utilizing generative AI such as Language Model.

Communication Language
English/JapaneseMust Requirements
Basic skills in programming● Basic knowledge in computer science●

Preferred Requirements
Development experience in applications and web services. When applying,please provide a portfolio, including examples of applications.
● Ability to independently plan and conceptualize applications● Communication skills in Japanese●↑ Back to list of themes

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GD02
[4-weeks Group Development] Data Science
Engineering
Area: Company Wide
Course: 4-weeks Group Development Course
You will be working on a task that involves predicting precipitation using
cutting-edge meteorological radar data called MP-PAWR in a team with 3 to 4
people. We will evaluate not only the accuracy of the precipitation forecast but also
the insights and observations obtained through the data analysis process.

Communication Language
English/JapaneseMust Requirements
Familiarity with Python● Basic knowledge in computer science●

Preferred Requirements
Communication skills in Japanese● Experience in data analysis●↑ Back to list of themes

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JE04
[10-Days] Employment Experience in theDevelopment and Operation of a web system for
Matlantis, a general-purpose atomic-level simulator
Area: Material Science / Matlantis
Course: 10-days Short Development Course
Work on the development and operation of Matlantis features. (e.g., improving
release methodology, investigating and optimizing API Gateway scheduling,cross-document search, developing JupyterLab extensions, etc.)

Communication Language
English/JapaneseMust Requirements
Basic knowledge in computer science● Familiarity with Python or Go●

Preferred Requirements
Experience with cloud computing (AWS/GCP/Azure, etc.)● Knowledge of container orchestration systems (Kubernetes)● Client and server model implementation (REST/gRPC/GraphQL, etc.)● Experience with tools that enable Infrastructure as Code (Terraform /CloudFormation)●↑ Back to list of themes

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RD01
Machine learning research for foundation model
Area: Foundation Model
Course: 7-weeks Research & Development Course
Category: Research Internship
We are looking for a self-driven candidate to join us in our research on foundation
models, including LLM. Prospective topics may include Training dynamics,
In-context learning / Chain-of-Thoughts as well as comprehensive investigation ofexisting models and datasets.
We welcome students who can independently set research themes and carry out
research projects.

Communication Language
English/JapaneseMust Requirements
Familiarity with the Python programming● Experience in Deep Learning framework such as PyTorch● Good understanding of machine learning●

Preferred Requirements●

Knowledge about a foundation model↑ Back to list of themes

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RD02
Building a Video-Based World Model
Area: Foundation Model
Course: 7-weeks Research & Development Course
Category: Research Internship
We aim to construct a neural network model that can learn the rules governing its
environment from video data, enabling it to predict future events and take action
accordingly.

Communication Language
English/JapaneseMust Requirements
Experience in implementation using deep learning frameworks such as
PyTorch● Theoretical knowledge in relevant fields such as algebra, operators, harmonic
analysis, signal analysis, etc.
● Strong interest in recent research related to invariance/equivariance on
machine learning●

Preferred Requirements
Currently enrolled in a doctoral program● Co-responsible paper(s) accepted at premier international conferences or in
English scientific journals (not necessarily limited to the field of machinelearning)● Research experience in the fields of physics and computer vision●↑ Back to list of themes

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RD03
Automating Dataset Generation for LLM Pretraining
Area: Foundation Model / Language
Course: 7-weeks Research & Development Course
Category: Project Internship
You will develop methods for automatically generating and filtering training datasets,
aimed at achieving high-efficiency and high-quality LLMs. This project focuses on
the following three items:(1) Thorough investigation of the data's impact on performance(2) Development of dataset generation methods to maximize training efficiency(3) Pre-training of LLM using the generated dataset

Communication Language
English/JapaneseMust Requirements●

Basic knowledge of computer science●

Experience in developing applications using LLMs, regardless of scale

Preferred Requirements●

Experience in software development within a team setting●

Accomplishments in the field of Machine Learning, such as paper
presentations or achievements in competitions●

Specialized knowledge in Natural Language Processing (NLP)

9●Experience with pre-training LLMs

↑ Back to list of themes

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RD04
Acceleration of pretraining for large language model
Area: Foundation Model / Language
Course: 7-weeks Research & Development Course
Category: Project Internship
Accelerate performance of pretraining for deploying LLMs. We assume theimplement MoE (Mixture of Experts) or expanding context length in this theme.

Communication Language
English/JapaneseMust Requirements
Basic knowledge in computer science● Coding skills in Python●

Preferred Requirements
Experience in accelerating in deep learning application● Experience in implementing Transformers model, and/or knowledges of themodel● Experience implementing distributed parallel application with multi-GPUs indeep learning●↑ Back to list of themes

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RD05
Alignment of large language model
Area: Foundation Model / Language
Course: 7-weeks Research & Development Course
Category: Project Internship
Conduct R&D regarding the alignment / instruction tuning of LLMs (Large Language
Models).
We mean by alignment of LLMs a process of ensuring them to produce desired
outputs people want to use in real-life applications.
Specifically, we are considering themes such as:
Evaluation of data quality by LLM, etc. (AI feedback)● Synthetic data generation by LLM● Fine-tuning of LLM by reinforcement learning (DPO, PPO, etc.)●

Communication Language
English/JapaneseMust Requirements
Basic knowledge in machine learning● Coding skills in Python● Experience in Deep Learning framework such as PyTorch●

Preferred Requirements●

Knowledge and experience in LLMs

↑ Back to list of themes

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RD06
Development of text embedding models
Area: Foundation Model / Language
Course: 7-weeks Research & Development Course
Category: Project Internship
Develop text embedding models for downstream tasks such as RAG and textclassification.

Communication Language
English/JapaneseMust Requirements
Basic knowledge in computer science● Coding skills in Python●

Preferred Requirements
Knowledge in natural language processing● Experience in Hugging Face Transformers and Sentence Transformers●↑ Back to list of themes

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RD07
Lightweight inference of large language models
Area: Foundation Model / Language
Course: 7-weeks Research & Development Course
Category: Project Internship
Accelerate inference and reduce memory usage in deploying LLMs. Our goal is toenable the deployment of 100B/1T-class LLMs on lightweight computational
resources through various approaches, including model compression techniquessuch as distillation, pruning, and quantization, as well as improvements in
implementation layers.

Communication Language
English/JapaneseMust Requirements
Basic knowledge in computer science● Coding skills in Python●

Preferred Requirements
Experience in accelerating and/or reducing memory usage in deep learning
deployment● Knowledge of model compression techniques such as distillation, pruning,and quantization● Coding skills in C++ and/or CUDA
● Experience with distributed data processing in multi-GPU environments●↑ Back to list of themes

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RD08
Implementation of a task in the field of computer
vision to a vision foundation model
Area: Foundation Model / Vision
Course: 7-weeks Research & Development Course
Category: Project Internship
Implement a task in the field of computer vision, such as semantic segmentation or
instance segmentation, in a vision foundation model. Specific tasks and approaches
to implementation will be decided in consultation with team members.

Communication Language
English/JapaneseMust Requirements
Basic knowledge of computer vision● Programming in Python●

Preferred Requirements
Research and development experience in the field of computer vision● Experience in research and development related to the machine learning field● Basic knowledge of large language models● Experience with deep learning frameworks●↑ Back to list of themes

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RD10
Development on Measurement and Analysis for theComputing Infrastructure
Area: Computing Infrastructure / Cluster
Course: 7-weeks Research & Development Course
Category: Project Internship
You will work on research and development on measurement and analysis systems
for the computing infrastructure. Your main task would be:● Fine-grained network measurement utilizing (by telemetry or FPGA)● Traffic engineering for RoCEv2● Development of a job generator for infrastructure evaluation● Fine-grained power measurement for accelerator based systems

Communication Language
English/JapaneseMust Requirements●

Basic knowledge of computer science

Preferred Requirements
Knowledge and experience of computer network● Knowledge and experience of kubernetes● Experience of FPGA/Microcontroller development●↑ Back to list of themes

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RD11
Development of MN-Core Compiler
Area: Computing Infrastructure / MN-Core
Course: 7-weeks Research & Development Course
Category: Project Internship
You will work on MN-Core compiler and related toolchain improvements. Your maintask would be:● Improvement of MN-Core compiler algorithm● Development toolchain for MN-Core

Communication Language
English/JapaneseMust Requirements
Basic knowledge of computer science● Experience of Python, C++●

Preferred Requirements
Experience of low level performance optimization● Knowledge and experience of deep learning compiler stack●↑ Back to list of themes

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RD12
Development ML/HPC Application on MN-Core
Area: Computing Infrastructure / MN-Core
Course: 7-weeks Research & Development Course
Category: Project Internship
You will work on ML/HPC application porting for MN-Core accelerator. Your maintask would be development for :● Library for HPC (e.g. LAPACK)● HPC Application (e.g. Atmospheric Model, Molecular Dynamics or RNA
secondary structure prediction)● ML workload
You can collaborate with compiler-core developer if you need it.

Communication Language
English/JapaneseMust Requirements
Basic knowledge of computer science● Experience of Python, C++●

Preferred Requirements
Experience of low level performance optimization● Experience on application development/porting for accelerator-based HPC
system●↑ Back to list of themes

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RD13
Development of framework and library for deploying
deep learning models in real world
Area: Computing Infrastructure / Ecosystem
Course: 7-weeks Research & Development Course
Category: Project Internship
Develop compiler/runtime (PFVM) that optimizes computational graphs of deeplearning models to perform inference performantly (in terms of execution time andmemory usage), targetting on various backends such as CUDA or edge devices.

Communication Language
English/JapaneseMust Requirements
Programming in Python● Basic knowledge of computer science● Basic knowledge of compiler●

Preferred Requirements
Programming in C++● Experience on deep learning compilers (ONNX, LLVM, MLIR, etc.)● Development experience of multi-pass compilers● Basic knowledge of optimization algorithms●↑ Back to list of themes

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RD14
Development of CuPy
Area: Computing Infrastructure / Cupy
Course: 7-weeks Research & Development Course
Category: Project InternshipIn this project, you will work on development of CuPy, an open-source array libraryfor GPU. (1) Development of new features for performance improvement, (2)Implementation of a prototype for new GPU backends (e.g., Metal, oneAPI), (3)Enhancing SciPy compatible APIs.

Communication Language
English/JapaneseMust Requirements
Programming in CUDA
● Programming in Python and NumPy● Basic knowledge of computer science●

Preferred Requirements
Experience with distributed data processing in multi-GPU environments(NCCL, MPI, etc.)● Development experience with Metal Shading Language●↑ Back to list of themes

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RD15
AutoML for LLM
Area: Computing Infrastructure / AutoML
Course: 7-weeks Research & Development Course
Category: Project Internship
You will join the development of AutoML technologies aimed at maximizing theperformance of LLMs. Specifically, you will work on algorithm development andexperiments on the following themes: (1) Prompt optimization, (2) Hyperparameter
optimization of systems utilizing LLMs.

Communication Language
English/JapaneseMust Requirements
Basic knowledge of computer science● Experience in developing applications using LLMs●

Preferred Requirements
Experience in software development and support in a multi-person team
● Achievements in the field of LLM, such as paper presentations andcompetition results● Expertise on Natural Language Processing (NLP)●↑ Back to list of themes

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RD16
Development of Optuna and Optuna Dashboard
Area: Computing Infrastructure / Optuna
Course: 7-weeks Research & Development Course
Category: Project Internship
You will work on the automatic hyperparameter optimization framework, Optuna andOptuna Dashboard, focusing on (1) implementing new algorithms, (2) improving
software quality and design, and/or (3) enhancing open-source software (OSS)
operations.

Communication Language
English/JapaneseMust Requirements
Basic knowledge of computer science

Preferred Requirements
●
●
●
●
●
●

Contribution to open-source software development, or similar achievements
Experience in software development and support in a multi-person teamAchievements in the field of machine learning, such as paper presentations
and competition results
Expertise on Bayesian optimization and black-box optimization algorithms
Experience in modern web front-end development
Experience in UI/UX design↑ Back to list of themes

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RD17
Applied research and development of machinelearning and atomic simulation on materials
Area: Material Science
Course: 7-weeks Research & Development Course
Category: Research InternshipWill work on the development of machine learning based atomic simulator, or theapplication of machine learning to molecular dynamics calculations and materials
exploration techniques. In addition to this theme, there is another theme which
focuses more on implementation and development (RD18). You can discuss whichtheme to join after application.
Reference: Matlantis https://matlantis.com/

Communication Language
English/JapaneseMust Requirements
Basic knowledge of computer science● Basic knowledge or interest of physic, chemistry, or material science●

Preferred Requirements
Knowledge of college degree in physics, chemistry, or materials science● Computational science experience. For example, in-depth knowledge of andexperience implementing and using simulation techniques such as quantum
chemical calculations, molecular dynamics methods, etc.
● Experience in research and development and writing papers on the
integration of physical simulation and machine learning●↑ Back to list of themes

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RD18
Implementation of material discovery algorithms in
Matlantis
Area: Material Science / Matlantis
Course: 7-weeks Research & Development Course
Category: Research Internship
You may conduct tasks such as implementing and evaluating computational
chemistry and data science-based methods for physical property or structure
predictions. In addition to this theme, there are also themes that focus more on
research (RD17), service provision (RD19), and inference API provision (RD20). We
can discuss which theme to join after application.
Reference: Matlantis: https://matlantis.com/

Communication Language
English/JapaneseMust Requirements
Coding skills in Python● Basic knowledge of computer science● Interest in physics, chemistry, or material science●

Preferred Requirements
Motivation to bring value to customers and connect academic research toactual products● College-level knowledge in physics, chemistry, or materials science● Computational chemistry experience. For example, knowledge or usage ofsimulation techniques such as quantum chemical calculations, molecular
dynamics methods, etc.
● Research or publication experience●↑ Back to list of themes

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RD19
Development and Operation of a web system for
Matlantis, a general-purpose atomic-level simulator
Area: Material Science / Matlantis
Course: 7-weeks Research & Development Course
Category: Project InternshipWork on the development and operation of Matlantis features. (e.g., improving
release methodology, investigating and optimizing API Gateway scheduling,cross-document search, developing JupyterLab extensions, etc.)

Communication Language
English/JapaneseMust Requirements
Basic knowledge in computer science● Familiarity with Python or Go●

Preferred Requirements
Experience with cloud computing (AWS/GCP/Azure, etc.)● Knowledge of container orchestration systems (Kubernetes)● Client and server model implementation (REST/gRPC/GraphQL, etc.)● Experience with tools that enable Infrastructure as Code (Terraform /CloudFormation)●↑ Back to list of themes

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RD20
Enhance monitoring and improve the PFP API in
Matlantis, a general-purpose atomic-level simulator.
Area: Material Science / Matlantis
Course: 7-weeks Research & Development Course
Category: Project InternshipWork on strengthening the monitoring of the most critical inference API in the
Matlantis service to discover bottlenecks and optimize the speed of inference.

Communication Language
English/JapaneseMust Requirements
Basic knowledge in computer science● Familiarity with Python or Go●

Preferred Requirements
Experience with cloud computing (AWS/GCP/Azure, etc.)● Knowledge of container orchestration systems (Kubernetes)● Client and server model implementation (REST/gRPC/GraphQL, etc.)● Experience with tools that enable Infrastructure as Code (Terraform /CloudFormation)●↑ Back to list of themes

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RD21
Research on Neural Network Potential(NNP) in Drug
Discovery
Area: Drug Discovery
Course: 7-weeks Research & Development Course
Category: Research InternshipIn recent years, there has been a growing demand for research on NNP in drug
discovery. Additionally, there is a need for further improved accuracy, addition of
new functionalities, and further validation in drug discovery. In this program, you will
work to solve these challenges by utilizing the Preferred Potential (PFP) developedby our company and ENEOS Corpo., or by developing a novel NNP. You CANNOT
apply in duplicate with RD22 or RD23.

Communication Language
English/JapaneseMust Requirements
Knowledge of basic computational science● Experience in implementing deep learning models using frameworks like
PyTorch.
●Preferred Requirements
Experience in Quantum Chemistry● Knowledges in Chemistry, Biology, or Physics●↑ Back to list of themes

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RD22
Research on machine learning models of proteinsand biomolecules for drug discovery
Area: Drug Discovery
Course: 7-weeks Research & Development Course
Category: Research Internship
You will work on machine learning models of proteins and biomolecules related todrug discovery.
Specifically, you will (1) conduct research on the application of protein language
models (pLM) to drug discovery, (2) protein design, and (3) study models forgenerating 3D structures and sequences of proteins and other biomolecules. You
CANNOT apply in duplicate with RD21 or RD23.

Communication Language
English/JapaneseMust Requirements
Knowledge of basic computer science● Expertise in chemistry, biology or physics (e.g. organic chemistry / medicinal
chemistry / structural biology / molecular simulation)●

Preferred Requirements
Research background in deep learning (papers/conference presentations arehighly desirable)● Experience in applying machine learning and data science in the fields ofchemistry, biology, or physics (papers and conference presentations arehighly desirable)● Experience in writing papers and implementing models and algorithms●↑ Back to list of themes

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RD23
Improving drug-likeness in machine learning based
Drug Design
Area: Drug Discovery
Course: 7-weeks Research & Development Course
Category: Research Internship
When designing a potential candidate for a drug, drug likeness is used to quantify aset of key molecular characteristics that have to be considered. It is favorable toaccurately predict such values and incorporate it in the designing process. Potential
research directions for this topic are: 1) Finding an effective way to condition thegenerative process with drug-likeness or similar metric, 2) making more accurate
predictions. You CANNOT apply in duplicate with RD21 or RD22.

Communication Language
English/JapaneseMust Requirements
Knowledge of basic computer science● Expertise in chemistry, biology or physics (e.g. organic chemistry / medicinal
chemistry / structural biology / molecular simulation)●

Preferred Requirements
Research background in deep learning (publication record/conference
presentations are highly desirable)● Experience in predicting, interpreting or analysis of
ADME/Pharmacokinetics/Pharmacodynamics/toxicity data.
● Experience in applying machine learning and data science in the fields ofchemistry, biology, or physics (papers and conference presentations arehighly desirable)● Experience in writing papers and implementing models and algorithms●↑ Back to list of themes

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30

RD25
High-Dimensional Tabular Data Generation
Area: Health Care
Course: 7-weeks Research & Development Course
Category: Project Internship
Tabular data generation can be useful in many applications, including healthcare. In
general, tabular data has various types of attributes taking continuous values ordiscrete values. However, existing results typically use benchmark datasets, whichhave a small number of attributes (d <= 50). In this internship theme, we want to
explore the performance of tabular data generation methods in higher dimension (d> 100) setting.

Communication Language
English/JapaneseMust Requirements
Familiarity with the Python programming● Experience in Deep Learning framework such as PyTorch● Good understanding of machine learning●

Preferred Requirements
Capability of propelling your R&D projects proactively and in a self-motivatedmanner: identify problems, analyze causes, devise solutions● Experiences in writing a technical paper●↑ Back to list of themes

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RD26
Research on machine learning methods for medical
imaging
Area: Health Care
Course: 7-weeks Research & Development Course
Category: Research InternshipWe will develop machine learning methods to solve problems in the medical
imaging domain (e.g. applications of foundation models, domain generalization,
survey of novel models etc.).

Communication Language
English/JapaneseMust Requirements
Deep knowledge of image analysis methods● Coding skills in Python● Experience using deep learning frameworks such as PyTorch●

Preferred 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●↑ Back to list of themes

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RD27
Reconstruction, editing, and generation of 3Dmodels and free-viewpoint videos
Area: Entertainment / PFN 3D/4D Scan
Course: 7-weeks Research & Development Course
Category: Project Internship
At PFN, we focus on improving and extending the technology related to 3D
reconstruction (https://pfn3d.com/) and free-view rendering(https://pfn3d.com/4d/index.html). Our research and development covers various
areas including machine learning-based reconstruction techniques as well as
various editing and generation technologies.
You CANNOT apply in duplicate with RD28.

Communication Language
English/JapaneseMust Requirements
Proficiency in coding using Python.
● Experience with deep learning frameworks such as PyTorch.
● Knowledge in computer vision and 3D-related topics.
●

Preferred Requirements
Knowledge and research experience in 3D reconstruction, free viewpoint
image generation, CG, AR/VR, video production, and camera-related topics.
● Experience with 3DCG production software.
●↑ Back to list of themes

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RD28
Reconstruction, editing, and generation of 3Dmodels and free-viewpoint videos
Area: Entertainment / PFN 3D/4D Scan
Course: 7-weeks Research & Development Course
Category: Research Internship
At PFN, we focus on improving and extending the technology related to 3D
reconstruction (https://pfn3d.com/) and free-view rendering(https://pfn3d.com/4d/index.html). Our research and development covers various
areas including machine learning-based reconstruction techniques as well as
various editing and generation technologies. We highly encourage students who
have the ability to independently conduct research projects.
You CANNOT apply in duplicate with RD27.

Communication Language
English/JapaneseMust Requirements
Proficiency in coding using Python.
● Experience with deep learning frameworks such as PyTorch.
● Knowledge in computer vision and 3D-related topics.
●

Preferred Requirements
Track record of presenting research at international conferences and/or
publishing in academic journals.
● Knowledge and research experience in 3D reconstruction, free viewpoint
image generation, CG, AR/VR, video production, and camera-related topics.
● Experience with 3DCG production software.
●↑ Back to list of themes

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RD30
Application of Generative AI Technologies toCreativity
Area: Entertainment / Creative
Course: 7-weeks Research & Development Course
Category: Research Internship
Engage users; inspire creators.
In this project, you will participate in research and development at Entertainment
team that aims to realize new creative expressions through the fusion of humancreativity and AI technology.

Communication Language
English/JapaneseMust Requirements
Required skills:● Coding ability using Python
In addition to the above, one of the following specialist skills or practical experience:● Development experience using machine learning libraries such as PyTorch● Research accomplishments in the field of machine learning, such as paper
publications● Advanced implementation ability and development experience in frontend /backend / applications

Preferred Requirements
Experience in OSS activities, R&D, or paper writing related to generative AI
technology● Practical experience in Kubernetes or cloud computing● Experience in system design, product design, or UI/UX design● Interest in entertainment (anime, games, etc.)●↑ Back to list of themes

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RD39
Research and Development of Advanced
Segmentation Methods for SAR Images
Area: Satellite Image
Course: 7-weeks Research & Development Course
Category: Project Internship
SAR (Synthetic Aperture Radar) images, capturing ground conditions by irradiating
electromagnetic waves from satellites and aircraft, are expected to be applicable fortasks like verifying the condition in times of disaster due to their ability to operate
under any weather or night conditions. In this internship theme, your research will
involve using domain adaptation, weakly-supervised learning, and self-supervised
learning to improve segmentation task performance by leveraging a large amount of
remote sensing data (SAR and optical images from satellites and aircraft) obtained
from various sensors.

Communication Language
English/JapaneseMust Requirements
Proficiency in machine learning using deep learning● Experience in R&D of machine learning models using PyTorch● Capability to tackle research issues proactively and ambitiously●

Preferred Requirements
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Experience in R&D related to computer vision, particularly segmentation
Technical understanding and R&D experience related to satellite images
Knowledge about domain adaptation, weakly-supervised learning, andself-supervised learning
Experience in development with multi GPUs
Experience in code management utilizing Git/GitHub
Experience using GIS data and related libraries
Having experience with Docker/Kubernetes
Ability to actively communicate within a small teamExperience in writing research papers
Readiness to engage in R&D activities that start from collection and36

preparation of datasets↑ Back to list of themes

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RD40
Research and development on theestimation/prediction of various weather conditions
using the advanced high-spatio-temporal resolution
3D weather data
Area: Meteorology
Course: 7-weeks Research & Development Course
Category: Project Internship
We are collaborating with NICT to analyze the advanced high-spatio-temporal
resolution 3D weather radar, which NICT developed recently. Interns will be
expected to engage research tasks that can take leverage of this rich and massive
radar data. Possible tasks are: quantitative precipitation estimation, precipitation
particle classification, lightening localization and prediction, 3D wind estimation, andothers.

Communication Language
English/JapaneseMust Requirements
Expertise in Deep Learning-based machine learning● Good experiences in developing machine learning models in PyTorch● Capability of propelling your R&D projects proactively and in a self-motivatedmanner: identify problems, analyze causes, devise solutions●

Preferred Requirements
Experiences in writing a technical paper● Knowledgeable about physics of whether and atmosphere● Mathematical skill to formulate and approximate physical phenomenon into
statistical models● R&D experiences in a multi-person team with Git, Github● R&D experiences with virtual container/environment such as Docker, andKubernetes● Development experiences on GPU clusters (private or public)● Experiences dealing with GIS data and related libraries, softwares●

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Accustomed to command-line interfaces in Linux and/or Mac● Eager to start R&D activities from collecting and organizing your own dataset● Team working and communications skills●↑ Back to list of themes

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RD41
Beyond BackPropagation : Attack on the Current
SGD based algorithms
Area: Deep Learning
Course: 7-weeks Research & Development Course
Category: Research Internship
We are looking for a candidate who will be excited to work with us on developing analternative to Back Propagation from both mathematical and computational
perspectives!

Communication Language
English/JapaneseMust Requirements
Interest in the alternatives to BackPropagation, Knowledge in related fields ofMathematics/Physics/CS, experience in programming (PyTorch).

↑ Back to list of themes

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RD42
Beyond Group Equivariant Framework: Extending
Neural Fourier Scheme to non-group structure
Area: Deep Learning
Course: 7-weeks Research & Development Course
Category: Research Internship
We are looking for a candidate interested in unsupervised learning of symmetry
strucutre. We are looking for a way to go beyond "group". Join us in our challenging
endeavor!

Communication Language
English/JapaneseMust Requirements
Knowledge in related Mathemtical Fields (Algebra, Category, Geometry, etc),
Programming experience, Interest in Symmetry learning and Equivariance Relation

Preferred Requirements
Research experience in the field of equivariance learning↑ Back to list of themes

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RD43
Optimizing the latent space for Flow basedalgorithms
Area: Deep Learning
Course: 7-weeks Research & Development Course
Category: Research Internship
We are looking for a way to choose "a good latent space" suited for the training offlow-based generative models, which are dominating the world today. We arelooking for candidates who are excited to work on this project from both atheoretical and computational perspective.

Communication Language
English/JapaneseMust Requirements
Theoretical Understanding of Flow-Matching/ Diffusion Models, or the knowledge in
related mathematical fields (SDE/PDE,etc) , Programming Experience

Preferred Requirements
Experience of learning a large model↑ Back to list of themes

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RD44
Extended Reality (VR, MR. AR) x AI
Area: HCI
Course: 7-weeks Research & Development Course
Category: Research InternshipResearch and develop novel XR experiences powered by AI, in particular LLMs andgenerative AI.

Communication Language
English/JapaneseMust Requirements
Experience developing XR applications

Preferred Requirements
Proven track record of XR application development, e.g. projects on Github,apps, games, research prototypes etc.
● Familiarity with generative AI tools and their APIs
● Publications at HCI/XR conferences or journals●↑ Back to list of themes

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RD45
Interactive Systems in HCI for Generative AI
Area: HCI
Course: 7-weeks Research & Development Course
Category: Research InternshipResearch and develop novel interactive systems for leveraging generative AI

Communication Language
English/JapaneseMust Requirements●

Research experience in HCI/ML/CV/NLP/Numerical Optimization

Preferred Requirements
Proven track record of interactive application development, e.g. projects on
Github, apps, games, research prototypes etc.
● Familiarity with generative AI tools and their APIs
● Publications at conferences or journals●↑ Back to list of themes

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