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Urgent! PhD Position F/M Cifre PhD - Mathematical and statistical modeling of cfDNA size fragments for diagnosis and prediction in oncology Job Opening In Marseille – Now Hiring INRIA

PhD Position F/M Cifre PhD Mathematical and statistical modeling of cfDNA size fragments for diagnosis and prediction in oncology



Job description

Contexte et atouts du poste

This Cifre PhD position will take place between Adelis technologies (Toulouse) and the Inria-Inserm team COMPO (COMputational Pharmacology in Oncology), located in the La Timone health campus.

The COMPO team comprises mathematicians, data scientists, pharmacists and clinicians and is a unique multidisciplinary environment focused on developing novel computational tools for decision-making in clinical oncology.

Mission confiée

Background


Liquid biopsy has established itself as a powerful tool for the early detection of cancer and the diagnosis, prognosis and treatment monitoring in a wide range of cancer types [1].

The BIAbooster technology from Adelis allows precise quantification of the size of cell free DNA fragments from plasma samples, establishing size distributions over a wide range of base pairs (part of the fragmentome).

In the SChISM (Size Cfdna Immunotherapies Signature Monitoring, N = 334 patients) study led by Pr S.

Salas (APHM and COMPO), we have demonstrated that features derived from these distributions were predictive of response to immunotherapy as univariable biomarkers [2, 3] or integrated within multivariable machine learning predictive models.


One of the main interest of such non-invasive biomarkers is for monitoring the response during treatment.

We have developed a first mechanistic model of the longitudinal data collected during SChiSM that demonstrated added value for prediction of progression [4].


Objectives

  • At Adelis, develop advanced signal processing methods to improve the information extracted from the cfDNA fragment size distributions

  • Develop mechanistic models of the dynamics of the cfDNA size distributions, coupled with tumor and immune variables

  • Implement a clinical decision tool for adaptive therapeutic decision
  • Methodology

  • Optimization of scalar or functional metrics derived from the cfDNA size distributions

  • Mathematical modeling of the system dynamics, first using ordinary differential equations, then with size-structured partial differential equations

  • Statistical modeling of the inter-individual variability through mixed-effects modeling

  • Joint modeling for association with time-to-event outcomes (progression-free or overall survival)
  • References:

  • Computational modeling for circulating cell-free DNA in clinical oncology
    L.

    Nguyen-Phuong, S.

    Salas, S.

    Benzekry
    JCO Clinical Cancer Informatics, 2025, 9

  • The SChISM study: Cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors
    Linh Nguyen Phuong, Frederic Fina, Laurent Greillier, Pascale Tomasini, Jean-Laurent Deville, Romain Zakrasjek, Lucie Della-Negra, Audrey Boutonnet, Frédéric Ginot, Jean-Charles Garcia, Sébastien Benzekry, Sébastien Salas
    under review, 2025

  • Long cell-free DNA fragments predict early-progression in patients with advanced or metastatic cancer treated with immune-checkpoint inhibition.
    Sébastien Salas, Linh Nguyen Phuong, Jean-Charles Garcia, Laurent Greillier, Caroline Gaudy-Marqueste, Jean-Laurent Deville, Audrey Boutonnet, Frédéric Ginot, Frédéric Fina, Sébastien Benzekry
    ASCO, 2024

  • Principales activités

    Main activities : Statistical data analysis
    Signal processing
    Mathematical modeling
    Statistical modeling (nonlinear mixed-effects modeling)
    Machine learning
    Programming
    Reporting: writing scientific papers, oral communications

    Additional activities :

    Review the literature
    Test and enhance the codebase
    Presentation to a non-technical audience
    Conceive apps for biomedical users (e.g. Shiny apps)
  • Compétences

    Technical skills and level required :

  • Excellent programming skills in a scripting language (R/python)

  • Strong background in mathematical modeling

  • Strong background in statistics and machine learning

  • Hands-on experience with real-world biomedical data analysis

  • Experience with nonlinear mixed-effects modeling is a plus

  • Experience in signal processing is a plus
  • Avantages

  • Subsidized meals

  • Partial reimbursement of public transport costs

  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)

  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours

  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)

  • Social, cultural and sports events and activities

  • Access to vocational training

  • Contribution to mutual insurance (subject to conditions)
  • Rémunération

    Duration: 36 months
    Location: Sophia Antipolis, France
    Gross Salary per month: 2200€ and 2300€ from 2026.


    Required Skill Profession

    Life Scientists



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