M/F PhD student in generative artificial intelligence for biological imaging (M/F)

CNRS - National Center for Scientific Research; France; On-site; EUR 20,000 - 27,000; Full time

Job summary

CNRS - National Center for Scientific Research, through IBENS at ENS-PSL, invites applications for a PhD student position in a multidisciplinary setting that combines AI, applied mathematics, biology, and image analysis. The successful candidate will join Auguste Genovesio's team, co-supervised with N.

Spassky's team, and will have access to GPU computing resources. The project focuses on deep generative models to jointly analyze tissue images and spatial molecular data, with opportunities to

Qualifications

• PhD candidate in AI, image analysis, or related field.
• Strong data analysis and programming skills.

Responsibilities

• Design, implement and evaluate generative models (flow matching, diffusion).
• Process and analyse large imaging datasets.
• Interact with partner biologists.
• Publish and present results at conferences.

Education

PhD candidate in AI / image analysis

Job description

Organisation/Company CNRS Department Institut de biologie de l'Ecole Normale Supérieure Research Field Mathematics Researcher Profile First Stage Researcher (R1) Application Deadline 27 Nov 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2027 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? No

Offer Description

Auguste Genovesio's team (image analysis and AI) at IBENS, ENS-PSL, co-supervised with N. Spassky's team (IBENS). Interdisciplinary environment (AI, applied mathematics, biology), access to GPU computing resources.

Deep generative models for the joint analysis of tissue images and spatial molecular data.
The PhD student will develop new deep generative learning methods to analyse biological tissue images coupled with spatial molecular measurements, and apply them to biological questions in collaboration with biology teams.

Activities: Design, implement and evaluate generative models (flow matching, diffusion); process and analyse large imaging datasets; interact with partner biologists; publish and present results at conferences.