PhD Candidate in Foundation Models for Medical Reasoning / PhD Candidate in Foundation Models for Medical Reasoning

PhD Position in Foundation Models for Medical Reasoning

100%, Basel, fixed-term

The research group led by Michael Moor is seeking to fill a PhD position focused on medical reasoning with foundation models. We are in search of an ambitious candidate eager to conduct groundbreaking research in medical AI. Our goal is to enhance the capabilities and reliability of medical reasoning models across diverse medical contexts. The successful candidate will join a pioneering research group that is at the forefront of developing medical foundation models, agent systems, and reasoning models.

Job Description

• Develop novel methods that advance the reasoning capabilities of medical foundation models (LLMs, MLLMs, and multi-agent systems).
• Design multimodal retrieval-augmentation strategies integrating EHRs, clinical guidelines, imaging databases, and other structured knowledge sources.
• Implement flexible memory and retrieval systems to support reasoning-chain generation and sequential decision-making in clinical tasks.
• Develop approaches to reduce hallucinations and improve reliability and robustness of medical AI systems, including adversarial robustness (in collaboration with SHS).
• Post-train and test-time scale multimodal foundation models at multiple scales.
• Co-develop a new benchmark for multimodal clinical reasoning and explainable sequential decision-making.
• Publish research results in top-tier ML venues (e.g., NeurIPS, ICML, ICLR) and top-tier biomedical journals (e.g., Nature Medicine, Nature Biomedical Engineering).
• Participate in collaborations across the MLCARE consortium (including funded secondments to a Max Planck Institute and Siemens).
• Engage in dissemination, open-source contributions, and knowledge-transfer activities.

Profile

• Master's degree in Computer Science, AI, Machine Learning, Mathematics, Electrical Engineering, or a closely related field; or
• Master's degree in Medicine (MD) with strong Python skills and some ML experience.
• Strong programming skills in Python and experience with modern ML stacks (PyTorch, HuggingFace, distributed training).
• Experience in LLMs, vision-language models, multimodal learning, or clinical NLP is highly welcomed.
• For computational candidates: experience in large-scale ML training (multi-node setups, distributed training) is a strong advantage.
• Good computational engineering practices: version control, reproducible pipelines, batch job management, etc.
• Ability to work independently, contribute to team efforts, and communicate effectively in English.
• Prior research experience, publications, or industry ML experience are pluses but not required.

Workplace

The successful candidate will be employed in a highly interdisciplinary environment at the Department of Biosystems Science and Engineering (D-BSSE), located in Basel, a major hub for medical and biomedical research and biotechnology.

We Offer

• A full-time, fully funded MSCA Doctoral Network position at ETH Zurich, one of the world's leading research universities.
• Access to cutting-edge computational resources, including large GPU clusters, and medical and biomedical collaborations.
• Participation in the Europe-wide MLCARE consortium for exchange and collaborations.
• Engagement with the ETH AI Center and SwissAI initiative, providing access to a vibrant and world-class AI community.

We Value Diversity and Sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity, and nurture a working and learning environment where the rights and dignity of all our staff and students are respected. Sustainability is a core principle for us as we work towards a climate-neutral future.

Curious? So Are We!

We look forward to receiving your online application. Please apply online using the form below, including the following documents (concatenated into one PDF):

• CV
• Bachelor and Master transcripts
• Motivation letter (motivation & fit with the program and the host lab)
• Letters of recommendation (if available; a list of names that can be queried for letters of recommendation will suffice)

Only applications matching the job profile will be considered.

About ETH Zurich

ETH Zurich is one of the world's leading universities specializing in science and technology. We are renowned for our excellent education, cutting-edge fundamental research, and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections worldwide, we work together to tackle the global challenges of today and tomorrow.