PhD Position in Machine Learning for Preterm Lung Health (100%)

Your assignments

• 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.

We offer you

• A PhD at the interface of machine learning, clinical medicine, and environmental health, with direct translational impact.


• Access to a unique longitudinal birth cohort spanning infancy to adolescence, plus international validation cohorts.


• Close supervision and collaboration with clinical and technical experts.


• Access to state-of-the-art HPC infrastructure.


• Enrolment in the Graduate School Medical and Health Sciences (GMHS) , with training and conference opportunities.


• Supportive team culture and employment conditions in accordance with the University of Basel.