Postdoc: AI for DNA Replication Genomics & IPLS

EURAXESS Ireland; Marseille; Hybride; EUR 36 000 - 48 000; Plein temps

Résumé du poste

The CNRS Institut de Biologie du Développement de Marseille invites applications for a postdoctoral researcher to join the RepliLand project, an interdisciplinary effort at the interface of genomics, artificial intelligence and biophysics.

The mission is to develop predictive models of the replication initiation probability landscape (IPLS) from limited data, with applications in cancer research and cross‑species genomics; fluency in English is essential.

Qualifications

• PhD in computational biology, machine learning, bioinformatics or a related field.
• Proficiency with Python programming.
• Proven experience in deep learning (PyTorch or TensorFlow).
• Previous experience with high-dimensional biological data and knowledge in (epi)genomics will be appreciated.
• The ability to work both independently and as part of a team, in an interdisciplinary context at the interface between information sciences and life sciences.
• Strong skills in scientific communication; written and oral; English is essential.

Responsabilités

• Develop and train deep learning models to predict IPLSs from multi-omics datasets (epigenomics, transcription, sequence).
• Using representation learning to extend the predictive framework and enable transfer learning across species.
• Apply explainability methods to extract biological insight from trained models.
• Write scientific articles and stay up to date with publications.

Connaissances

Python programming

Deep learning (PyTorch/TensorFlow)

Bioinformatics

Interdisciplinary teamwork

Formation

PhD in computational biology / ML / bioinformatics

Outils

PyTorch

TensorFlow

Description du poste

The CNRS Institut de Biologie du Développement de Marseille invites applications for a postdoctoral researcher to join the RepliLand project, an interdisciplinary effort at the interface of genomics, artificial intelligence and biophysics.

The mission is to develop predictive models of the replication initiation probability landscape (IPLS) from limited data, with applications in cancer research and cross‑species genomics; fluency in English is essential.