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

Academic Positions; Schweiz; Vor Ort; CHF 55.000 - 70.000; Vollzeit

Zusammenfassung

The Analytics & Informatics for Child Health (AICH) group at the University of Basel invites applications for a PhD position focused on machine learning for pediatric health. You will lead multimodal ML to integrate clinical, physiological, environmental, and biomarker data, building interpretable models and validating across international cohorts.

You will collaborate with clinicians, epidemiologists, and exposure scientists, contribute to reproducible code, publish in ML and respiratory

Qualifikationen

• MSc in Computer Science, Data Science, Statistics, Biomedical Engineering, or related field.
• PhD at the interface of ML, clinical medicine, and environmental health is preferred.
• Strong programming skills in Python and ML frameworks.
• Experience with tabular or longitudinal data and model validation.

Aufgaben

• Lead the machine learning component: integrating clinical, physiological, environmental, and biomarker data.
• Model lung function trajectories from infancy to adolescence for preterm children.
• Perform external validation with different cohorts and assess generalisability.
• Publish in ML and clinical/respiratory venues; present at international conferences.
• Collaborate closely with clinicians, epidemiologists, and exposure scientists; contribute to reproducible, well-documented code.

Ausbildung

MSc in Computer Science, Data Science, Statistics, Biomedical Engineering, or related field

PhD in ML/clinical medicine/environmental health

Python programming

PyTorch and scikit-learn

Data science methods and statistical modelling

Communication with non-technical collaborators

Tools

PyTorch

scikit-learn

Jobbeschreibung

Organisation/Company University of Basel Application Deadline 28 Nov 2026 - 22:59 (UTC) Country Switzerland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Department of Biomedical Engineering (DBE), University of Basel

Group: Analytics & Informatics for Child Health (AICH) in collaboration with University Children's Hospital Basel (UKBB)

Starting date: November 2026 or upon agreement

Duration: 4 years

About Us

The Analytics & Informatics for Child Health (AICH) group develops AI/ML methods, digital tools, and secure data pipelines to advance pediatric healthcare. We work at the intersection of clinical medicine, machine learning, and data-intensive research, collaborating closely with hospitals, clinical IT teams, and research partners at the University of Basel.

The Project

Preterm birth affects around 14 million infants each year and is a major risk factor for lifelong respiratory disease. Current definitions of bronchopulmonary dysplasia (BPD) are categorical and predict long-term outcomes poorly. Robust early-life markers to identify infants at the highest risk of prematurity-associated lung disease (PLD) are lacking.

This project uses the Basel-Bern Infant Lung Development (BILD) cohort, which combines infant lung function, breath metabolomics, urine oxidative stress markers, clinical history, high-resolution air pollution exposure, and longitudinal lung function follow-up into adolescence. The goal is a continuous, physiology-based risk stratification framework to replace categorical BPD definitions, and to inform a respiratory follow-up program for preterm children in Switzerland.

The PhD student will lead the machine learning component: integrating clinical, physiological, environmental, and biomarker data to identify early-life signatures, aetiotypes, and treatable traits of PLD, with external validation in cohorts from Italy and Australia.

• 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.
• 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.
• 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.

Application / Contact

Please submit the following as a single PDF:

• CV
• MSc and BSc transcripts
• Contact information for 2-3 references
• (Optional) Links to GitHub, thesis, or previous technical work

Applications should be sent via the University of Basel application portal.