EPFL Machine Learning for Biomedicine Lab Opens Research Opportunities for Postdoctoral Fellows, PhD Students and Interns in Switzerland (Fully Funded)

Overview

The Machine Learning for Biomedicine Lab at the École Polytechnique Fédérale de Lausanne in Switzerland offers research opportunities for postdoctoral fellows, doctoral students, summer research interns, and bachelor and master students. The lab focuses on developing artificial intelligence methods that improve the understanding of biological systems and enable scientific discoveries through the analysis of complex and multimodal biological data.

Research areas include foundation models for single-cell biology, multimodal learning, generative models of biology, cell engineering, agentic artificial intelligence for scientific discovery, and the underlying principles of existing artificial intelligence models. The laboratory provides access to GPU computing resources through the Swiss AI Initiative and participates in research networks such as the CIFAR Multiscale Human Program, the Immunological Genome Project, and the Cancer Immunotherapy Networking Program.

Who can apply

Postdoctoral applicants must hold, or be nearing completion of, a doctorate in computer science, computational biology, statistics, applied mathematics, or a closely related discipline. The lab welcomes individuals with backgrounds in artificial intelligence for biology, fundamental artificial intelligence research, and computational biology who demonstrate scientific curiosity and a strong research track record. Previous experience working with biomedical data is helpful but not mandatory for candidates with a strong artificial intelligence background.

Doctoral applicants can seek admission through EPFL doctoral programmes in computer and communication sciences or computational and quantitative biology. Bachelor and master students can apply for summer research internships through the Summer at EPFL programme or pursue research projects and thesis work directly at the lab.

Requirements

Postdoctoral applicants must submit a curriculum vitae including a publication list, a research statement of one to two pages covering previous research and future plans, the names and contact details of two references, and a preferred start date. These materials must be sent by email with the subject line Postdoctoral application.

Bachelor and master students applying for specific research projects are expected to meet role-specific qualifications. Projects involving graph and point cloud pattern mining require machine learning experience, proficiency in Python, and familiarity with PyTorch, with basic knowledge of single-cell biology considered an advantage. Projects focused on improving agents for single-cell genomics require strong experience in post-training large language models, particularly reinforcement learning, with experience in agentic workflows being a significant advantage.