Postdoctoral Researcher in Machine Learning and Pediatric Health

About the role

Postdoctoral position at the University of Basel applying machine learning to pediatric health. The role sits between Basel’s medical faculty and a clinical group, working with real patient data from one of Switzerland’s largest children’s hospitals. Position is 80 to 100 percent, two to three years initial appointment. Deadline 28 May 2026.

Responsibilities

• Develop and validate ML models on pediatric clinical and imaging data.
• Work directly with clinicians to translate models into decision support that clears regulatory and ethics review.
• Publish in venues such as Nature Medicine, JAMA, NeurIPS, and ML4H workshops.
• Co-supervise PhD students and Master’s projects in the group.

Requirements

• PhD in computer science, biomedical engineering, statistics, or a related quantitative field.
• Strong machine learning experience, especially with health or biomedical data.
• Python proficiency with PyTorch or JAX.
• Fluent English. German is welcomed but not required for the research work.

Nice to have

• Prior publications on pediatric, neonatal, or perinatal health data.
• Experience with federated learning, privacy-preserving ML, or differential privacy.
• Familiarity with Swiss or EU health-data regulations.

How to apply

Open the apply link and submit CV, publication list, two reference letters, and a one-page research statement on how your background fits the role. Submit before 28 May 2026.