Phd position M/F

Job Description

Thesis Subject

The PhD candidate will develop and apply AI/bioinformatics computational approaches to integrate heterogeneous spatial, molecular and imaging data, with the aim of identifying invasion-associated metabolic niches and understanding how tumour–microenvironment interactions are organised across GBM regions.
The candidate will be embedded in the ShadowEV-GBM MIC consortium, which generates and analyses high-dimensional datasets including spatial transcriptomics, mass spectrometry imaging, metabolomics/lipidomics, extracellular-vesicle-associated molecular profiles, experimental GBM models and patient-level information. The project will also explore how tissue-level metabolic signatures may connect to clinically accessible readouts, including metabolic MRI-derived imaging features, in order to open translational perspectives.

This rich data context offers an opportunity to investigate questions such as:

- How can spatial omics and molecular profiles be integrated to describe metabolic organisation in GBM?
- Can AI methods identify reproducible metabolic niches associated with invasion and tumour–microenvironment communication?
- Which spatial or molecular signatures are conserved across experimental models, tissue-level data and features in patient cohorts?
- Can interpretable computational scores derived from spatial and molecular data be related to clinical imaging phenotypes, including metabolic MRI features?
- How can multimodal AI approaches support biomarker discovery while remaining biologically interpretable?

The PhD candidate will design and apply integrative computational workflows using methods such as multi-omics integration, spatial modelling, representation learning, network-based analysis, unsupervised learning and interpretable AI. The project leaves room for methodological exploration while remaining closely connected to biological interpretation, experimental validation and translational perspectives within the consortium.

Your Work Environment

Glioblastoma (GBM) is an aggressive brain tumour whose progression is strongly influenced by communication between tumour cells and the brain microenvironment. These interactions are spatially organised, particularly along invasion routes enriched in neurons, oligodendrocytes and myelin-derived material, and may shape metabolic programs that support tumour dissemination and resistance.
Understanding these processes requires AI and bioinformatics methods able to integrate heterogeneous, high-dimensional data. The ShadowEV-GBM project brings together spatial omics, metabolomics/lipidomics, molecular profiling and experimental models to study metabolic communication in GBM invasion. In this context, spatial transcriptomics and mass spectrometry imaging provide tissue-level maps of tumour–microenvironment organisation, while MRI metabolic imaging offers a patient-scale perspective on related metabolic and invasive phenotypes.

This PhD research will be carried out at the IBGC as part of the ShadowEV-GBM collaborative project. Under the joint supervision of Dr Nikolski and Dr Engelhardt, the candidate will become an integral part of both teams.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions