Postdoctoral Position in Modeling the Spatial and Temporal Variation of the Microenvironment

SFBI; Montpellier; Sur place; EUR 35 000 - 45 000; Plein temps

Résumé du poste

A research institute in Montpellier is looking for a talented postdoctoral fellow to work on modeling spatial and temporal variations of cellular networks. The successful candidate will utilize machine learning techniques to analyze data related to autoimmune diseases and cancer. Applicants with a PhD in bioinformatics or related fields are encouraged to apply to join a dynamic team of researchers.

Qualifications

• PhD with strong interests in life science applications.
• Solid machine learning skills required.
• Experience in deep neural networks is a plus.

Responsabilités

• Develop new methods to map molecular networks in autoimmune diseases.
• Model time evolution of cellular networks.
• Integrate data over networks to extract biological information.

Connaissances

Machine learning

Graph neural networks

Deep neural network

Bioinformatics

Data analysis

Formation

PhD in Bioinformatics

PhD in Mathematics/Physics/Computer Science

Description du poste

Postdoctoral Position in Modeling the Spatial and Temporal Variation of the Microenvironment

spatial biology systems biology deep learning cellular networks microenvironment

Description

The Cancer Bioinformatics and Systems Biology team at IRCM (Cancer Research Institute of Montpellier) is looking for a talented postdoctoral fellow.

Cellular networks and their spatial heterogeneity play an important role in multiple diseases. In this project, we want to develop new methods based on graph neural networks and auto-encoders to map molecular networks in the context of autoimmune diseases and cancer. In the case of a particular autoimmune disease (rhumatoid arthritis, RA), we will also have access to data acquired at multiple time points, thereby offering the opportunity to model time evolution of cellular networks in this pathology. The primary data types will be spatial trancriptomics and single-cell transcriptomics. In some cases (e.g., RA), transcriptomics will be complemented with spatial metabolomics or other data types (e.g., cytometry, proteomics).

Our team has developed a number of algorithms and machine learning models to infer both intra-cellular and cellular networks [1–4], and to integrate data over such networks to extract actionable biological information such as candidate targets or biomarkers [5–6], including in single-cell and spatial transcriptomics [7,8]. The successful candidate will address the challenging question of spatial and temporal variation of such neworks. Through our participation in several funded multidisciplinary projects, access to fresh data and involvment of highly competent biologists and clinicians is granted.

Preferred qualifications are either a bioinformatics PhD and solid machine learning skills or a mathematics/physics/computer science PhD with strong interests in life science applications. Deep neural network practical experience would be a plus. The position is funded for 2 years. A first contract will be established for 1 year and extended upon performance.

Interested applicants should e-mail their CV, a letter of motivation and the names and e-mails of 2 references to Prof Jacques Colinge ([e-mail]). Application deadline is December 10, 2025. Expected starting date February or March 1, 2026.

References

Villemin J‑P, Bassaganyas L, Pourquier D, Boissière F, Cabello‑Aguilar S, Crapez E, et al. Inferring ligand‑receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. Nucleic Acids Res. 2023; gkad352. doi:10.1093/nar/gkad352

Cabello‑Aguilar S, Alame M, Kon‑Sun‑Tack F, Fau C, Lacroix M, Colinge J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020. doi:10.1093/nar/gkaa183

Villemin J‑P, Giroux P, Maillard M, et al., Colinge J. Addressing multiple facets of ligand‑receptor network inference including single-cell proteomics. bioRxiv, 2025. doi:10.1101/2025.10.05.680519

Borg J‑P, Colinge J, Ravel P. Testing and overcoming the limitations of modular response analysis. Brief Bioinform. 2025. doi:10.1093/bib/bbaf098.

Alame M, Cornillot E, Cacheux V, Tosato G, Four M, Oliveira LD, et al. The molecular landscape and microenvironment of salivary duct carcinoma reveal new therapeutic opportunities. Theranostics. 2020;10: 4383–4394. doi:10.7150/thno.42986

Blomen VA, Majek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, et al. Gene essentiality and synthetic lethality in haploid human cells. Science. 2015;350: 1092–1096. doi:10.1126/science.aac7557

Giguelay A, Turtoi E, Khelaf L, Tosato G, Dadi I, Chastel T, et al. The landscape of cancer‑associated fibroblasts in colorectal cancer liver metastases. Theranostics. 2022;12: 7624–7639. doi:10.7150/thno.72853

Honda CK, Kurozumi S, Fujii T, Pourquier D, Khellaf L, Boissiere F, et al. Cancer‑associated fibroblast spatial heterogeneity and EMILIN1 expression in the tumor microenvironment modulate TGF‑β activity and CD8+ T‑cell infiltration in breast cancer. Theranostics. 2024;14: 1873–1885. doi:10.7150/thno.90627

Candidature

Procédure : Interested applicants should e-mail their CV, a letter of motivation and the names and e-mails of 2 references to Prof Jacques Colinge ([e-mail]).

Contacts