Call for Interns

OMRON SINIC X (OSX) is looking for research interns throughout the year to work with our members in challenging research projects on a variety of topics related to robotics, machine learning, computer vision, and HCI. Many students have participated in our internship program, and their achievements have been published as academic papers at international conferences such as CVPR, ICML, IJCAI, ICRA, CoRL, or as OSS libraries. For more information about our activities at OSX, please visit Medium and GitHub

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Research on robot learning through large-scale skill database

We are seeking interns to work on developing a new robot learning method based on constructing a large-scale skill database. In this project, we will focus on designing and implementing scalable algorithms using GPGPU to handle the large-scale data. Our goal is to publish research papers at international conferences and release a widely used library based on our research results.

  • Required skills and experience
    • Research Experiences using PyTorch
    • Programming Experiences using GPUs
    • Programming Experiences with C++/CUDA
    • Fundamentals of Optimal Transport and Search Algorithms
    • Research and development experiences in machine learning
  • Preferred skills and experience
    • Advanced expertise in CUDA programming
    • Programming experiences in Cython/pybind/nanobind
    • Advanced expertise of performance analysis using GPU/CPU profilers
    • Publication recors in the field of robotics(CoRL,ICRA,IROS),machine learning (ICML,NeurIPS,ICLR) and relevant fields (SGIR).
  • Machine learning
  • Search algorithm
  • CUDA

Understanding and Application of Multimodal Healthcare Data using Machine Learning

In the field of healthcare, applications of machine learning are shifting from inference based solely on understanding time-series data or medical images to aiming for integrated understanding of multiple data types with different attributes and structures. In this project, research and development will be conducted on the understanding and application to multimodal data (such as image, audio, biosignal, video, table, text, etc.), and writing papers aiming for acceptance at international conferences in related fields.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
    • Knowledge and experience in computer vision
  • Preferred skills and experience
    • Knowledge and experience in XAI
    • Knowledge and experience in modality fusion (e.g. CLIP)
  • Machine learning
  • Computer vision
  • Signal processing
  • Natural language processing
  • Algorithm
  • Multimodal understanding

an LLM-based personalized conversational agent

The current LLM excels in conversational and problem-solving abilities as a dialogue agent. However, it lacks capabilities such as learning user habits and long-term memory, making it insufficient as an intelligent companion. This study aims to achieve a personalized conversational agent that can provide users with a pseudo-experience characterized by being "friendly, intelligent, and exhibiting a sense of growth" through the construction of LLM's long-term memory and learning of rules.

  • Required skills and experience
    • Python縲；ithub縲．ocker
    • Knowledge and experience in LLM
  • Preferred skills and experience
    • LangChain
    • Vector Database
    • Knowledge Graph
    • RAG
    • LLM Finetuning
    • LLM Agent
    • LLM related development experience
  • Interaction
  • Natural language processing
  • Development
  • LLM

Multimodal understanding of specialized documents

Understanding technical documents such as papers and patents require understanding data, including structured text and diagrams. It requires efforts that go beyond the framework of conventional natural language processing. In this project, we will conduct research and development on a multimodal understanding of such specialized documents and write papers for international conferences in related fields.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Knowledge and experience in natural language processing
    • Mathematical knowledge and formulation ability in machine learning and deep learning
  • Computer vision
  • Natural language processing
  • Multimodal understanding

Research on human-in-the-loop machine learning

Machine learning research that incorporates humans in machine learning and makes efficient use of feedback from humans is expanding. In this project, we will conduct research and development on human-in-the-loop machine learning research and write papers aiming for publication in top international conferences in machine learning and interaction or journals.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Knowledge and experience in human-computer interaction
  • Machine learning
  • Interaction
  • HCI
  • HRI
  • HAI

Research on law discovery from observed data

Research on causal analysis on time-series data and explanatory AI is progressing to make some predictions while clarifying the laws between data. For example, symbolic regression for science discovery is one of the research topic. In this project, we will conduct research and development on methods from a new angle for discovering such laws and write papers aiming for acceptance in top international conferences in machine learning or journals. The internship is mainly aimed at Ph.D. students and is expected to last at least three months.

  • Required skills and experience
    • Python, Github, Docker
  • Preferred skills and experience
    • Knowledge and experience in natural language processing
    • Knowledge and experience in signal processing
  • Machine learning
  • Data mining

Research on learnable discrete information processing

Research to modify specific computational modules by making them machine-learnable is underway (e.g., differentiable rendering) to include them in a deep learning pipeline. This project will develop research on making discrete information processing learnable and writing papers for international machine learning conferences, such as ICLR, ICML, and NeurIPS.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Mathematical knowledge and ability to formulate methods for machine learning and deep learning
    • Expertise in convex optimization
  • Machine learning
  • Algorithm
  • Optimization

Learning soft robotic tool manipulation with tactile sensors

(Scheduled to start from September 2024 ) This project aims to make a physical soft robot with tactile sensors perform contact-rich and dextrous tasks using tools. To this end, high-dimensional tactile information processing and data-efficient learning approaches that learn the control policy with a few training data will be required.

We would like the accepted interns to develop the robot learning algorithm with tactile sensor fusion, implement robot software, perform experiments, and write papers.
We aim to submit top-tier robotics or AI conference or journal papers such as (ICRA, IROS, CoRL, RA-L, T-RO, and NeurIPS). The mentors will meet with the interns at least once a week to discuss the research progress, plans for paper submissions, and distribution of writing tasks to ensure a more reliable submission.

The following themes are relevant but not limited. Themes will be flexibly determined based on the intern's expertise. The project actively seeks interns with experience in machine learning, reinforcement learning development, and a strong interest in robotics applications. The project assumes on-site work in Tokyo.
繝ｻSoft robotic manipulation learning
繝ｻTactile-based manipulation using vision-based or distributed tactile sensors
繝ｻOffline and online reinforcement learning
繝ｻSim-to-real transfer learning

Accepted papers that interns contributed as first author advised by this mentor: ICRA2024 (2 papers), IROS 2023 (2 papers), NeurIPS 2023, IEEE ACCESS, IEEE CASE 2021, and CoRL 2020.
Related projects: learning robotics assembly using soft wrist and tactile sensors (https://omron-sinicx.github.io/saguri-bot-page/).

  • Required skills and experience
    • Experience in Python or C++

    At least one of the following:

    • Research and development experiences in robot learning, sensing, control theory, or motion planning
    • Research and development experiences using machine learning or reinforcement learning
  • Preferred skills and experience
    • Research and development experience in ROS
    • Experience in submitting papers of the field of robotics and artificial intelligence
    • Experience in participation in robot competitions
  • Machine learning
  • Robotics
  • Signal processing
  • Algorithm
  • Development
  • Soft robotics
  • Tactile sensing

Development of a manipulator driven by all motors at the base link

We will develop a lightweight manipulator driven by all motors at the base link like our previous manipulator (https://omron-sinicx.github.io/twistsnake/).

  • Required skills and experience
    • Experience in robotic mechanism design
    • Publication in robotics (IROS, ICRA, etc.)
  • Preferred skills and experience
    • Robot competition
    • Experience in team-based development
    • Experience of receiving an award from an academic society or a scholarship
    • Experience of coding in ROS, Python, or C++ in the development of a robot
  • Robotics
  • Mechanics design

Development of an Explainable Language-Based System Control Framework

We are at the dawn of the social utilization of applications based on LLMs, such as LangChain. In this project, we aim to create a framework for "trustworthy" system control through language directives, capturing market share opportunities unique to this period. We plan to develop and release an OSS (Open Source Software) that generates reusable and shareable processes through user interactions. Additionally, we aim to submit our work to the OSS Track of international conferences.

  • Required skills and experience
    • Knowledge of Python sufficient to understand the internals of PyTorch
    • Basic knowledge of git
  • Preferred skills and experience
    • Knowledge of optimization methods like linear programming
    • Experience contributing to open-source software (any level is acceptable)
    • Experience in utilizing LLMs for coding
  • Interaction
  • Algorithm
  • Development

Research on縲application of machine learning methods to physics simulation

We will conduct research and development on the application of your machine learning methods to physics simulations and write papers in related fields (Nature/Science, Physical Review, SC/HPCG, etc.).

  • Required skills and experience
    • Knowledge and experience in machine learning
  • Preferred skills and experience
    • Knowledge and experience in physics simulation e.g. DFT/MD/Tensor Network
    • Pytorch, Python
    • Github, Docker
  • Machine learning
  • Physics simulation

Research on machine learning for few training data

In modalities and domains where are pre-trained models so-called foundation models, target tasks can be achieved by finetuning on a small number of data. On the other hand, more advanced machine learning techniques are required for tasks not in such modalities or domains. In this project, we will conduct research and development on such small-data machine learning and write a paper aiming at top international conferences in machine learning and computer vision.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Knowledge and experience in computer vision
  • Machine learning
  • Transfer learning
  • Domain adaptation

Realizing Deep Graph Neural Networks Effective for Dense Graphs

In general, Graph Neural Networks (GNNs) are known to lose unique information per vertex due to oversmoothing when the edge density is high. The traditional method to address this issue, known as WeaveNet, has problems with high memory consumption and computational redundancy. This project aims to solve these issues to make it applicable to practical-sized problems and to tackle various unresolved issues for general GNNs, with the goal of submitting to the most prestigious international conferences in the field of machine learning.

  • Required skills and experience
    • Knowledge of PyTorch sufficient to implement GNNs
    • Basic knowledge of git
  • Preferred skills and experience
    • Knowledge of important areas such as combinatorial optimization, which are challenging for GNNs
  • Machine learning
  • Algorithm
  • Graph Neural Network

Research automation for machine learning

You will be working on an AI that performs machine learning research autonomously.
To date, we have been working on the automatic improvement of machine learning algorithms in the project AutoRes (https://www.autores.one/) and have obtained budding results. In this project, we aim to further develop this direction and create a system that automatically proposes machine learning algorithms and verifies their improved performance.

  • Required skills and experience
    • Experience in Python development.
    • Experience in machine learning development
  • Preferred skills and experience
    • Knowledge of large-scale language models.
    • Experience writing papers in machine learning related fields.
  • Machine learning
  • Natural language processing
  • Algorithm
  • Development
  • Large language models (LLM)
  • Research Automation

Representation learning for structured data

Pre-training models based on supervised/self-supervised learning using large amounts of data are widely used in machine learning for natural language and images. The concept of foundation models is gaining ground. In this project, we will conduct research and development on representation learning of data with unique structures other than images and natural language and write a paper aiming at a journal such as Nature and Science families.

  • Required skills and experience
    • Python, Github, Docker
  • Preferred skills and experience
    • Knowledge and experience in representation learning with images/natural language.
    • Knowledge and experience in machine learning with point clouds/graphs.
    • Mathematical knowledge of machine learning and deep learning and ability to formulate equations
  • Machine learning
  • Representation learning
  • Point cloud processing
  • Graph processing

Research on 3D vision including visual SLAM and NeRF

In this research project, we will pursue new models and optimization techniques for image-based 3D sensing technologies such as Visual SLAM and NeRF. We aim to publish papers at top international conferences in the computer vision field, such as CVPR, ICCV, and ECCV.

  • Required skills and experience
    • Research and/or development experience in deep learning using PyTorch, etc.
    • Good mathematical understanding about 3D geometries
    • Python
  • Preferred skills and experience
    • Knowledge and experience in 3D deep learning or classical VSLAM
    • Knowledge and experience in munerical optimization
    • Implementation skills of custom forward&backward functions and GPU kernels in PyTorch, etc.
    • Programming skills in C++
    • Knowledge and experience with GitHub/GitLab and Docker
  • Machine learning
  • Computer vision
  • Algorithm
  • 3D vision
  • Optimization

Participation and Management of Competitions Related to Fixed-Viewpoint Video Analysis Technology

We are recruiting interns to participate in and manage various activities leading up to the Competition-based Workshop to be held at the top image processing conference in 2025. This includes both participating in and organizing the competition.

  • Required skills and experience

    At least one of the following:

    • Participation in Competition-based Workshops
    • Research experience in Vision & Language technology
    • Research experience in Human-Object Interaction detection technology
  • Preferred skills and experience
    • Experience in organizing Competition-based Workshops
    • Research experience in analyzing work videos
  • Machine learning
  • Computer vision
  • Natural language processing
  • Vision&Language
  • Procedure Understanding
  • Human-Object Interaction Detection

Research on application of machine learning to physics simulation method and results

We will conduct research and development on the application of machine learning to the algorithms or the data analysis of physics simulations, e.g. DFT/MD/Tensor Network, and write papers to journals e.g. Nature/Science, Physical Review, or conferences e.g. SC/ICML.

  • Required skills and experience
    • Knowledge and experience in physics simulation e.g. DFT/MD/Tensor Network
  • Preferred skills and experience
    • Knowledge and experience in machine learning
    • Pytorch, Python
    • Github, Docker
  • Machine learning
  • Physics simulation

Research on fusion understanding of image/video and natural language

While there are tons of research on machine learning for understanding images and natural language, deep learning has led to the commoditization of modules from each other, and research combining multiple modalities is also increasing. In this project, we will develop research on fusion understanding of image/video and natural language and write papers for relevant top international conferences.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Knowledge and experience in natural language processing
    • Knowledge and experience in computer vision
  • Machine learning
  • Computer vision
  • Natural language processing
  • Multimodal understanding

Versatile peg insertion skill learning

We will develop a framework for learning versatile peg insertion based on our model based reinforcement learning methods, such as transfer learning (https://kazutoshi-tanaka.github.io/pages/transam.html), model switching (https://kazutoshi-tanaka.github.io/pages/smmrl/), sub-task transition using tactile information (https://omron-sinicx.github.io/saguri-bot-page/).

  • Required skills and experience
    • Publication record in human-computer interactions (ICRA, IROS, CoRL, ICML, NeurIPS, ICLR, etc.)
    • Experience in manipulation learning
    • Experience of coding in ROS, Python, or C++ in research
  • Preferred skills and experience
    • Management of projects and code using git/GitLab/GitHub
    • Experience in team-based development
  • Machine learning
  • Robotics
  • Manipulation
  • Model based reinforcement learning

Robust Image Recognition Model

Image recognition models are noted to be vulnerable to domain shifts caused by environmental changes. This project aims to construct robust image recognition models resilient to environmental changes. We plan to select research topics on robust image recognition models from various fields, including but not limited to generalization of image classification models and applications such as Vision-Language models, and aim to submit them to international conferences.

  • Required skills and experience
    • Experience in Python development.
    • Experience in developing machine learning model for image recognition
  • Preferred skills and experience
    • Knowledge of image recognition model.
    • Knowledge of transfer learning and multi-modal model.
    • Experience writing papers in related fields.
  • Machine learning
  • Computer vision
  • Natural language processing
  • Algorithm
  • Domain Generalization
  • Image recognition
  • Multi-modal model
  • Robust

AI for Science

You will work on AI research that accelerates and automates research and development itself. You will participate in partial projects in the realization of AI scientists who can formulate research claims, run experiments, analyze the results, and write papers in an interactive co-evolution with human researchers.

  • Required skills and experience
    • Python, Github, Docker
    • Knowledge and experience in deep learning
  • Preferred skills and experience
    • Knowledge and experience in natural language processing, computer vision, and data science
    • Mathematical knowledge and formulation ability in machine learning and deep learning
  • Machine learning
  • Interaction

Research on high-dimensional black box optimization

Black-box optimization, such as Bayesian optimization, is subject to computational overheads as the number of parameters to be optimized increases. In this project, we will develop research on high-dimensional black-box optimization and write papers for top international conferences in machine learning or journals.

  • Required skills and experience
    • Python, Github, Docker
  • Preferred skills and experience
    • Knowledge and experience with black box optimization such as Bayesian optimization
  • Algorithm
  • Optimization

Conditions

Term: From 3-month duration (assuming 5 working days a week). Start and end dates can be adjusted. Some projects accept short-term interns from 1-month duration.
Hours: Full-time or part-time (e.g. 3 days a week, etc. negotiable). 45-minute breaks. Holidays and weekends off.
Location: On-site, hybrid, or remote options are available. Hybrid and remote options are only available if you live in Japan; due to legal issues, we cannot pay salaries to remote interns who live outside of Japan. If you join our internship program, you must come to Japan. In such cases, we offer support for travel expenses. Some internship projects may require on-site work. In this case, you will be assigned to one of our offices in Hongo or Shinagawa.
Salary: Full-time monthly salary ranges from 240,000 JPY to 480,000 JPY. Hourly rate is applied for part-time work. Social security and other benefits are provided according to the working conditions. Transportation and housing expenses are fully covered. In addition, other expenses necessary for research activities (PC, laptop, etc.) are fully supported.
Language: Japanese or English (English-only communication is also fine.)
Others: Two or more mentors with extensive research experience will provide in-depth support for each project. Computational resources (workstations, and server clouds with GPUs) and robotic facilities (robotic arms, various sensors, 3D printers, motion capture systems, and other prototyping and experimental equipment) are available.

How to apply

Please fill the application form. We will first screen each application based on those information.
For other inquires, please contact internships@sinicx.com. We will first screen each application based on those information.

Those who pass the above screening will be interviewed remotely. Please prepare slides or other materials to introduce past research and development activities and achievements.

Please contact us at least three months in advance if you need a visa to enter Japan.