Job offer

European Commission; France; Sur place; EUR 35 000 - 45 000; Plein temps

Résumé du poste

A leading research institution in France is seeking a highly motivated Postdoctoral Researcher with expertise in computational biology and AI to support projects on lung cancer. The successful candidate will work on integrating multi-omics data for predictive modeling, contributing to precision oncology. The role requires a PhD in a related field and proficiency in data analysis tools such as Python and R. Join a multidisciplinary team and impact translational research in oncology.

Prestations

Access to state-of-the-art facilities

Opportunities for international collaborations

Conference presentations

Qualifications

• Proficiency in multi-omics data analysis, machine learning, and statistical modeling.
• Experience with tools such as Python/R, scikit-learn, TensorFlow/PyTorch, and Bioconductor.
• Passion for translational research in oncology and immunotherapy.

Responsabilités

• Develop and analyze computational models for lung cancer data.
• Integrate multi-omics data for biomarker discovery.
• Collaborate with a multidisciplinary research team.

Connaissances

Computational Biology

AI

Machine Learning

Mathematical Modeling

Multi-Omics Data Analysis

Statistical Modeling

Programming (Python/R)

Biomarker Discovery

Translational Research

Formation

PhD in Computational Biology, Bioinformatics, AI/ML, or Applied Mathematics

Outils

Python

R

scikit-learn

TensorFlow

PyTorch

Bioconductor

Description du poste

Organisation/Company INSERM U1260 Research Field Biological sciences » Biology Medical sciences » Cancer research Computer science » Other Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Positions Postdoc Positions Country France Application Deadline 31 Aug 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? Yes

Offer Description

Project Overview

We are seeking a highly motivated Postdoctoral Researcher with strong expertise in computational biology, AI, machine learning, and mathematical modeling to join our multidisciplinary team. The position will support two major projects on lung cancer biology and treatment resistance, integrating multi-omics data (genomic, epigenomic, spatial transcriptomic, and clinical) for predictive and mechanistic modeling.

This postdoctoral project focuses on developing advanced computational models and AI-driven tools to integrate and analyze multi-omics data related to lung cancer. The core objective is to identify biomarkers and build predictive models for early cancer detection, treatment response, and toxicity risk. The work combines three complementary research areas: understanding the early stages of squamous cell lung carcinogenesis using spatial transcriptomics and methylation/exome data from archived bronchial biopsy samples; identifying biomarkers of resistance or sensitivity to immune checkpoint inhibitors in non-small cell lung cancer (NSCLC) using immunohistochemistry, exome sequencing, and clinical data; and building predictive models for treatment efficacy and toxicity in elderly NSCLC patients by integrating data from tumor tissue (RNA-seq, NGS, immunofluorescence), blood samples (cfDNA and cytokines), from the host (SNPs) and clinical indicators (e.g., Lung Immune Prognostic Index (LIPI) score). This work will lead to robust, validated computational pipelines and machine learning models that support biomarker discovery and precision oncology applications.

Candidate Profile

• PhD in Computational Biology, Bioinformatics, AI/ML, Applied Mathematics, or related fields
• Proficiency in multi-omics data analysis, machine learning, and statistical modeling
• Experience with tools such as Python/R, scikit-learn, TensorFlow/PyTorch, Bioconductor
• Strong background in omics integration, biomarker discovery, and survival modeling
• Passion for translational research in oncology and immunotherapy
• Ability to work in a multi-disciplinary, translational research team.
• Excellent communication skills (working language: English; French is a plus).

Expected Outcomes

• Publishable models for biomarker discovery and prediction.
• Integrated pipelines for multi-omic analysis of spatial and clinical datasets.
• Validated AI tools for cancer risk stratification, treatment response, and toxicity prediction.
• Contribution to translational research impacting clinical decision-making in thoracic oncology.

We Offer

• A dynamic, translational research environment at the interface of Strasbourg Hôpital, and EFREI Paris – Panthéon-Assas University.
• Access to state-of-the-art core facilities (spatial genomics, NGS, bio-computing clusters).
• Opportunities for international collaborations, conference presentations, and co-supervision of students.

Application

Please send a single PDF containing:

• Cover letter (research interests, fit for project)

For informal inquiries, feel free to reach out to Dr. Mathew or Pr. Mascaux. We look forward to your application! Join us in building the next generation of precision oncology tools.

Where to apply

E-mail [e-mail]

Requirements

Research Field Biological sciences » Biology Education Level PhD or equivalent

Research Field Medical sciences » Cancer research Education Level PhD or equivalent

Research Field Mathematics » Applied mathematics Education Level PhD or equivalent

Research Field Computer science » Other Education Level PhD or equivalent

Skills/Qualifications

Technical Skills Required

• Strong Programming: Python, R, and experience with libraries such as scikit-learn, XGBoost, TensorFlow/PyTorch, Bioconductor.
• Multi-Omics Analysis: Experience with transcriptomics, methylation, exome, IHC, and NGS data types.
• Mathematical Modeling: Statistical learning, logistic regression, multivariate analysis, dimensionality reduction, integration models.
• Data Integration & Cleaning: Proven ability to preprocess noisy biological data (e.g., FFPE artifacts, batch correction).
• Visualization & Interpretation: Heatmaps, PCA, UMAP/tSNE, ROC curves, model explainability tools (e.g., SHAP, LIME).
• Bonus: Experience with spatial transcriptomics or immune profiling.

Languages ENGLISH Level Excellent

Languages FRENCH Level Good

Research Field Computer science » OtherMedical sciences » Cancer research