EL

PostDoc Position at EPFL on Multimodal Foundation Models in Collaboration with Novartis

LausanneFull-timeOn site3 weeks ago

Muslim-Friendly Workplace Features

  • Prayer room
filled = mentioned in the job postingoutlined = stated on the employer's website

Features are hints taken from the job posting, not confirmed commitments. Employer statements come from the employer's website, not from this posting.

Sign up for free – what an account gives you
  • Browse all 3,000+ jobs with sourced Muslim-friendly hints
  • See the evidence: quote and source for every feature
  • Save jobs and track your applications
  • Personal recommendations based on your values
Sign up for free

🔔 New jobs like this one — straight to your inbox

No account needed. One email per week max, unsubscribe anytime.

By submitting you agree to receive emails about matching jobs from SalamJobs. Unsubscribe anytime.

💬 Or let Rafik search — your free career advisor →

📄Stellenbeschreibung

General

The Artificial Intelligence in Molecular Medicine (AIMM) Lab, led by Prof.

Charlotte Bunne at EPFL, sits at the interface of computer science and the life sciences, affiliated with both the School of Computer and Communication Sciences and the School of Life Sciences, as well as the EPFL AI Center.

As part of an industry collaboration with Novartis, the lab is opening a postdoctoral position on multimodal model development.

We are recruiting a postdoctoral researcher with strong expertise in large-scale AI development and a background in biology.

Working in close collaboration with Novartis, the position focuses on the development of multimodal biological foundation models: from core architecture design and pretraining at scale to integration into agentic interfaces.

The role comes with dedicated access to the Swiss National Supercomputing Centre (CSCS) and the Swiss AI Initiative.

Research directions include

Multimodal model development: novel pretraining objectives, tokenization, and scalable architectures that integrate diverse biological data modalities.
Multimodal representation learning: aligning and fusing information across modalities to capture biological systems at multiple scales.
Agentic interfaces: integrating multimodal foundation models into reasoning and decision-support systems.
Translational applications: bridging multimodal foundation model research towards impact in early drug discovery through public and Novartis-internal datasets.

We are looking for candidates with

A PhD in machine learning, computer science, computational biology, or a closely related field (completed or near completion).
Demonstrated experience in large-scale AI development, including pretraining, fine-tuning, or scaling on substantial compute.
A solid biological background; familiarity with multiple biological data modalities is expected.
A strong publication record at top-tier ML conferences and/or life science journals.
Excellent communication skills and enthusiasm for interdisciplinary research.

We offer

Dedicated compute allocation on CSCS and the Swiss AI Initiative.
Direct collaboration with the AI for Research team and core disease area teams at Novartis Biomedical Research.
Mentorship toward an independent academic or industry research career.
An international, collaborative environment at one of Europe's leading technical universities.

Information

Activity rate: 100%.

Contract: one year, with the plan of a one year renewal.

Location: Lausanne, Switzerland

Start date: ideally Fall 2026, with later start dates possible by arrangement.

To apply, please send a CV, a brief statement of research interests (1 page), and the names of two references. More information via aimm.epfl.ch. Applications are reviewed on a rolling basis.

Job overview

Posted
2 October 2026
Location
Lausanne
Employment type
Full-time
Work model
On site

Company

EL

EPFL – École polytechnique fédérale de Lausanne

Lausanne
Stated on the employer’s website

Interspiritueller Raum (Géode) der Aumônerie auf dem Campus, ausdrücklich zum Beten und allen offen — nicht muslimspezifisch, Beschäftigte nicht genannt

Evidence on file — sign in with a free account to see the quote and source.

Sign up free to view the evidence

Employer’s self-description, not a commitment for this job — bring it up in the interview.

View company profile
Source

This job listing comes directly from the employer’s career site and is hosted there.

View original listing