Discovering Relevant Dimensions in Epidemic Dynamics for Data-Efficient Predictive Modeling

Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP); France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A research institute in France is hiring a full-time postdoctoral researcher to develop machine and deep learning methods for epidemic modeling as part of the DiscoReel project. The ideal candidate will have a strong background in computer science, physics, or applied math, with experience in large machine learning models. No epidemiology experience is necessary, but a commitment to applying computational modeling for public health improvement is required.

Qualifications

• Experience in designing and training large machine learning models.
• Interest in applying computational modeling to improve public health.

Responsabilités

• Develop machine and deep learning methods for epidemic modeling.
• Integrate methods with mechanistic models.

Connaissances

Machine learning methods

Deep learning

Epidemic modeling

Computational modeling

Formation

Background in computer science, physics or applied math

Description du poste

Organisation/Company Institut Pierre Louis d'Epidémiologie et de Santé Publique (IPLESP) Department UMRS 1136 Research Field Medical sciences » Epidemiology Physics » Applied physics Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Country France Application Deadline 15 Dec 2025 - 23:00 (Europe/Monaco) Type of Contract Temporary Job Status Full-time 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

We are hiring a postdoctoral researcher to work full-time on the DiscoReel project. The postdoc will work on developing machine and deep learning methods for epidemic modeling, integrating them with mechanistic models and focusing on foundation models. The candidate should have a background in computer science, physics or applied math and previous experience in designing and traning large machine learning models. No prior experience in epidemiology is needed, but interest in applying computational modeling to improve public health is required.