Postdoctoral Position in Computational Modeling & Multi-Omics Integration for Lung Cancer Research

Job title:

Postdoctoral Position in Computational Modeling & Multi-Omics Integration for Lung Cancer Research

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Job description

Offer DescriptionProject OverviewWe 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.

ApplicationPlease send a single PDF containing: * Cover letter (research interests, fit for project)

• CV with publication list
• Contact details of two referees

Email to: and by August 31, 2025.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[email protected]RequirementsResearch Field Biological sciences » Biology Education Level PhD or equivalentResearch Field Medical sciences » Cancer research Education Level PhD or equivalentResearch Field Mathematics » Applied mathematics Education Level PhD or equivalentResearch Field Computer science » Other Education Level PhD or equivalentSkills/QualificationsTechnical 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 ExcellentLanguages FRENCH Level GoodResearch Field Computer science » OtherMedical sciences » Cancer researchAdditional InformationWork Location(s)Number of offers available 1 Company/Institute INSERM Country France City STRASBOURG Postal Code 67000 GeofieldContact CitySTRASBOURG WebsitePostal Code67000 E-Mail[email protected][email protected]STATUS: EXPIREDShare this page

Expected salary

Location

Strasbourg, Bas-Rhin

Job date

Fri, 15 Aug 2025 07:38:05 GMT

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