PhD: Multimodal ML for Preterm Lung Health & Trajectories

Universität Basel; Basel; On-site; CHF 48,000 - 56,000; Full time

Benefits offered by this job

PhD position

Longitudinal cohort data access

Close supervision

HPC infrastructure access

GMHS enrolment and training

Collaborative team culture

Job summary

Universität Basel invites applications for a PhD position in Analytics & Informatics for Child Health (AICH) focused on multimodal ML for neonatal and pediatric respiratory health. You will work with clinical data, breath metabolomics, biomarkers, and environmental exposure to derive early-life signatures and risk strata.

You will validate findings across international cohorts, publish in ML and clinical venues, and collaborate closely with clinicians and researchers.

Qualifications

• MSc in Computer Science, Data Science, Statistics, Biomedical Engineering, or a related field.
• Solid programming skills in Python and fluency with ML frameworks (PyTorch, scikit-learn).
• Experience with tabular or longitudinal data, missing data handling, and model validation.
• Interest in interpretable ML, clustering/phenotyping, or time-series/trajectory modelling.

Responsibilities

• 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 preterm populations.
• Perform external validation with different cohorts and assess generalisability.
• Publish in ML and clinical/respiratory venues; present at international conferences.

Skills

Python programming

ML modelling

Data integration

Communication & collaboration

Education

MSc in related field

Tools

PyTorch

scikit-learn

Jupyter

Job description

Universität Basel invites applications for a PhD position in Analytics & Informatics for Child Health (AICH) focused on multimodal ML for neonatal and pediatric respiratory health. You will work with clinical data, breath metabolomics, biomarkers, and environmental exposure to derive early-life signatures and risk strata.

You will validate findings across international cohorts, publish in ML and clinical venues, and collaborate closely with clinicians and researchers.