Missions

The agent will be responsible for building and update numerical platform that will bring together a range of digital technologies related to digital biology and artificial intelligence for the purposes of modeling and simulation. The study context will be metabolic disorders. Responsibilities include the development of new tools, the maintaining the operational readiness of digital tools and software, training users (engineers, technicians, and students), and developing technologies and processes. This mission also includes the implementation of artificial intelligence and bioinformatics tools necessary to assist in the phases of data mining, model optimization, extrapolation, and prediction in order to extract markers of interest.

Activités

The activities will consist of
- Collecting, organizing, and processing data from literature, databases, and experimental studies
- Implementing algorithms for learning and classifying collected data using artificial intelligence
- Collecting, cleaning, and updating mathematical models of systems biology in collaboration with research teams (e.g., metabolism, liver pathology, etc.)
- Developing new integrative models for prediction purposes
- Developing simulators in C++/CUDA for solving differential equations describing multi-species dynamics (e.g., ROS, TNFa, LPO, etc.).
- Create interactive graphical user interfaces (GUIs) in Python (CustomTkinter, Plotly) for real-time 3D monitoring and visualization of simulated data.
- Integration of artificial intelligence methods, including supervised machine learning (classification, regression) and deep learning (neural networks with TensorFlow and PyTorch) for predicting biomarker evolution and optimizing digital treatments. - Implementation of a complete simulation → visualization → interpretation pipeline to facilitate translational research.
- Deployment of Linux/Unix-compatible tools and integration into a distributed scientific environment.
- Identify the most appropriate digital biology tools for data storage (databases) and processing (statistical methods) to address the issue at hand.
- Process and analyze data using the appropriate tools used in systems biology and toxicology (biological networks), clinical data (images, dosages, etc.), and omics data (metabolomics, transcriptomics, proteomics, epigenetics, etc.).
- Adapt IT tools to digital biology models (such as metabolic flux models (MFA), PK-PD and PBPK pharmacokinetic models, and systems biology models based on the resolution of differential equations and parameter optimization), while using new data analysis tools adapted to specific needs, using programming languages such as R and Python.

- Keep abreast of scientific and technological advances in digital biology
- Participate in meetings and promote the results of analyses and model development (reports, publications, presentations)
- Interact with the unit's teams and participate in scientific meetings
- Advise and guide newcomers to the group.
- Monitor and organize scientific and technological developments, disseminate information about new tools within the group.

These technologies will be developed locally at LIMMS in Tokyo in collaboration with researchers from LIMMS and Prof. Sakai's laboratory.

Compétences

Skills

- High level of proficiency in scientific programming, particularly in Python, C++, and CUDA for parallel computing on GPUs.
- Expertise in artificial intelligence, including machine learning and deep learning, with practical experience using PyTorch, TensorFlow, and TensorBoard.
- Development of graphical interfaces for interactive scientific visualization (CustomTkinter, Plotly, 3D sliders for spatio-temporal tracking of simulations).
- Ability to work on interdisciplinary projects involving numerical simulation, 3D visualization, and AI analysis.
- In modeling, computational biology, and bioinformatics.
- General knowledge of biology, biochemistry, and metabolism.

The successful candidate must be able to
- Install the necessary tools on computing machines
- Program and train AI algorithms
- Master the tools/software necessary for digital biology
- Adapt to the specific needs of research projects
- Know how to carry out a study analysis (data mining, data preparation and integration, statistical analysis, interpretation, reporting of results, and communication with other researchers who are not bioinformaticians)

Motivation for teamwork, organization, and communication of information. Contact with suppliers, service providers, or external colleagues.
Desire to work in Japan in an international context
Languages: English: fluent spoken and written.

Contexte de travail

This position corresponds to a need for evolution in the LIMMS's activities and a need to support researchers in the field of digital progress and digital twins, as well as associated digital technologies. This is an initiative implemented as part of the ANR Track NAFLD.

This position context is within the LIMMS laboratory, an International Research Laboratory (IRL) between the CNRS (Institute of Systems Science and Engineering) and the University of Tokyo (Institute of Industrial Science), whose main location is in Tokyo.
Since 2019, LIMMS and Prof. Sakai's laboratory have promoted the integration of different data sources into predictive mathematical models in order to extrapolate experimental results and propose predictions in humans (liver disease, metabolic and pharmacokinetic coupling). LIMMS and Sakai lab are collaborating with clinician for a better development of the digital twin, mimicking the patients' situation.
The goal of this team is to:
- Reproduce digital twins of specific cells, tissues, organs, and patients
- Predict and assess the toxicity level of drugs, chemicals, pollutants, and, more generally, all agents that attack the human body
- Propose alternatives methods using numerical technologies

The technologies of this project should be developed either in Japan at Tokyo at LIMMS either in France in a clinical environment.

Contraintes et risques

Geological risk in Japan
Long exposure to screen and desktop work risk