PhD Position in Machine Learning for Preterm Lung Health

Your position

• Develop and apply multimodal ML methods to integrate clinical, breath metabolomics, biomarker, lung function, and environmental exposure data.
• Model lung function trajectories from infancy to adolescence, with a focus on moderate-to-late preterm children.
• Perform external validation with different cohorts and assess generalisability.
• Publish in ML and clinical/respiratory venues; present at international conferences.
• Work closely with clinicians, epidemiologists, and exposure scientists; contribute to reproducible, well-documented code.

Your profile

• MSc in Computer Science, Data Science, Statistics, Biomedical Engineering, or a related field.
• Solid programming skills in Python; fluency with ML frameworks (PyTorch, scikit-learn) and statistical modelling.
• You are curious about how early life shapes lifelong health, and motivated to build ML that clinicians will actually use.
• Experience with tabular or longitudinal data, missing data, and model validation. Experience with small-sample or clinical/epidemiological data is a strong plus.
• Interest in interpretable ML, clustering/phenotyping, or time-series/trajectory modelling.
• Interest in clinical research and respiratory or pediatric health; no medical background required.
• Rigour, independence, and the ability to communicate with non-technical collaborators.
• Fluent in English (spoken and written). German is an advantage but not required.