Doctoral Student in Machine Learning and Medical Data Science

Doctoral Student in Machine Learning and Medical Data Science

100%, Zurich, fixed-term

Are you passionate about machine learning and AI and excited to develop methods that can make a difference in healthcare? The Medical Data Science Group at ETH Zurich, led by Prof. Julia Vogt, is looking for a highly motivated Doctoral Student to join our interdisciplinary research team. The Medical Data Science Group develops innovative machine learning and AI methods to address challenging problems in medicine and healthcare. Our research combines foundational advances in machine learning with meaningful medical applications. Potential research directions include representation learning, pre- and post-training of foundation models, generative AI, explainability and interpretability, probabilistic modeling, time-series analysis, and multimodal learning. The Doctoral position offers the opportunity to pursue original research at the intersection of machine learning and healthcare, working closely with researchers from technical and medical disciplines. Specific research topics will be developed jointly with Prof. Julia Vogt, taking into account the candidates' interests and expertise as well as the group's research priorities.

Job description

• Conduct independent research on novel machine learning and AI methods, with a focus on medical and healthcare applications

• Design, implement, and systematically evaluate machine learning models and experiments, ensuring rigorous and reproducible research

• Publish and present your findings at scientific conferences and in peer-reviewed journals, and develop your doctoral thesis

• Collaborate with experts from technical and medical disciplines and contribute to scientific discussions, seminars, and group activities

• Support teaching activities and the supervision of student projects

Profile

• A Master's degree in computer science, machine learning, statistics, applied mathematics, or a closely related technical field

• Strong foundations in mathematics, statistics, and machine learning, combined with solid programming skills (e.g., Python)

• A strong interest in both foundational machine learning research and applications in medicine and healthcare

• Curiosity, creativity, and the ability to approach challenging research questions independently and systematically

• Strong analytical and problem-solving skills, with a commitment to rigorous and responsible scientific research

• Strong communication skills in English and enthusiasm for interdisciplinary, collaborative research

• Openness to new ideas, constructive feedback, and scientific exchange

• Experience with medical or biological data is an advantage, but not a requirement

Workplace

We offer

• An international and collaborative research environment at the intersection of machine learning, AI, and medicine

• The opportunity to shape your own research and work closely with experts from technical and medical disciplines

• Access to state-of-the-art infrastructure and high-performance computing resources at ETH Zurich

• Support for your scientific development through conference participation, training, and networking opportunities

• Opportunities to gain teaching, supervision, and mentoring experience

• Competitive ETH Zurich doctoral salary and employment conditions

• A meaningful career: ETH Zurich supports your professional development and actively contributes to positive change in society.

• Commitment to a sustainable and climate-neutral university

• You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits

chevron_right Working, teaching and research at ETH Zurich

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 in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future .

Curious? So are we.

We look forward to receiving your online application with the following documents:

• Cover letter describing your motivation, research interests, and experience

• Curriculum Vitae (CV)

• Academic transcripts and degree certificates (Bachelor's and Master's)

• Contact details of three academic or professional references (name, email address, affiliation)

• A publication, thesis, or summary sampling your academic reasoning and writing

Further information about our research and team can be found on our Website . Questions regarding the position should be directed to Dr. Silke Mühlstedt, silke.muehlstedt@inf.ethz.ch (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

The preferred application deadline is 22 November 2026.

Applications will be reviewed on a rolling basis, and applications received after this date will also be considered.

The preferred starting date is 1 January 2027 , or a later date by mutual agreement.

About ETH Zürich

ETH Zurich is one of the world's leading universities specialising 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 all over the world, we work together to develop solutions for the global challenges of today and tomorrow.