巴塞尔大学PhD Position in Machine Learning for Preterm Lung Health申请条件要求-申请方
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巴塞尔大学
PhD直招介绍
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.
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.
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申请要求
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.
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