Postdoctoral Researcher in Digital Twins Using Physics-Informed AI for Oncology

Updated: 23 days ago
Location: Montpellier, LANGUEDOC ROUSSILLON
Job Type: FullTime
Deadline: 27 Apr 2026

27 Mar 2026
Job Information
Organisation/Company

Universite de Montpellier
Department

Human Resources
Research Field

Mathematics
Researcher Profile

Recognised Researcher (R2)
Positions

Postdoc Positions
Application Deadline

27 Apr 2026 - 23:59 (Europe/Paris)
Country

France
Type of Contract

Temporary
Job Status

Full-time
Is the job funded through the EU Research Framework Programme?

Other EU programme
Reference Number

2026-R0230
Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

- work environment: The research environment at the University of Montpellier and the Computational Systems Biology team is dynamic, collaborative, and highly interdisciplinary. The team is based at the Laboratory of Pathogens and Host Immunity (LPHI), which is affiliated with CNRS and INSERM. It is part of an internationally recognized institution focused on cutting-edge research in computational biology, cancer, and infectious diseases.

The team combines expertise from multiple fields, including mathematics, physics, artificial intelligence (AI), bioinformatics, and biological sciences. Researchers work closely with experimental biologists to ensure that computational models are deeply rooted in real-world biological data. The collaborative approach allows for the development of predictive models that bridge the gap between theory and experiment, with a focus on high-impact areas such as cancer biology, drug discovery, and precision medicine.

Located in Montpellier, a vibrant city in southern France known for its historical significance and natural beauty, the university offers a stimulating and diverse academic environment. Researchers at the LPHI benefit from strong institutional support, access to state-of-the-art technologies, and opportunities for collaboration with leading research groups in France and internationally.

This environment fosters innovation and provides ample opportunities for career development through exposure to interdisciplinary research and cutting-edge scientific challenges.

- main mission: The main mission of this postdoctoral position is to develop multiscale mathematical models and digital twins to better understand the initiation of colorectal cancer (CRC) using organoid models. The researcher will focus on integrating dynamic mutations, live multiplexed transcriptomic imaging, and proteomic imaging at the cellular level to predict the effects of these factors at the tissue level. The position will also involve simulating and optimizing complex models that integrate biological and physical data using advanced methods, such as physics-informed neural networks (PINNs) and potentially generative adversarial networks (Pi-GANs). These models aim to predict cell fate and tumor development in CRC. The postdoc will collaborate with both computational scientists and experimental biologists, contributing to the advancement of predictive models for cancer initiation, as well as helping identify potential biomarkers for early detection and treatment in precision medicine.

- activities: The main activities for this postdoctoral position include:

  • Developing multiscale mathematical models to simulate the initiation of colorectal cancer (CRC) using organoid models. These models will integrate dynamic mutations, live transcriptomic imaging, and proteomic data.
  • Simulating and optimizing model parameters using advanced computational techniques, including physics-informed neural networks (PINNs) and potentially generative adversarial networks (Pi-GANs), to predict the evolution of cancer at both the cellular and tissue levels.
  • Collaborating with experimental teams to ensure the integration of biological data into the computational models, improving their accuracy and real-world applicability.

  • Where to apply
    E-mail

    ovidiu.radulescu@umontpellier.fr

    Requirements
    Research Field
    Mathematics
    Education Level
    PhD or equivalent

    Research Field
    Physics
    Education Level
    PhD or equivalent

    Research Field
    Computer science
    Education Level
    PhD or equivalent

    Skills/Qualifications
  • Educational Background:
    • PhD in Biophysics, Mathematics, Computer Science, or a related field.
    • Strong foundation in dynamical modeling, particularly ODEs, PDEs, and probabilistic models.
  • Technical Skills:
    • Proficiency in scientific computing and experience with programming languages such as Python, Julia, or Matlab.
    • Experience in mathematical modeling and computational simulations, particularly in the context of biological systems.
    • Familiarity with AI techniques, such as physics-informed neural networks (PINNs), is preferred but not essential.
    • Experience with high-dimensional data analysis (e.g., transcriptomics, proteomics) is a plus.
  • Research Skills:
    • Ability to develop and optimize multiscale models and integrate experimental data with computational frameworks.
    • Strong problem-solving and analytical skills for modeling biological systems and interpreting experimental results.
    • Experience in collaborating with multidisciplinary teams, including experimental biologists, physicists, and computational scientists.
  • Communication:
    • Proficiency in English (written and spoken) for scientific communication, including writing papers and presenting research findings.
    • Ability to communicate complex technical concepts to both technical and non-technical audiences.
  • These skills will enable the postdoc to contribute to cutting-edge research in cancer modeling, AI in oncology, and precision medicine.


    Additional Information
    Work Location(s)
    Number of offers available
    1
    Company/Institute
    Laboratory of Pathogens and Host Immunity (LPHI)
    Country
    France
    Geofield


    Contact
    City

    Montpellier
    Website

    http://www.umontpellier.fr/
    Street

    163 rue Auguste Broussonnet
    Postal Code

    34000

    STATUS: EXPIRED

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