PhD in Multimodal ML for Early-Life Lung Health

University of Basel; Basel; On-site; CHF 60,000 - 80,000; Full time

Benefits offered by this job

PhD enrollment in GMHS

GMHS training opportunities

Access to HPC infrastructure

Collaborative research environment

Job summary

University of Basel seeks a PhD student to lead machine learning for a pediatric lung health project, integrating clinical data, breath metabolomics, biomarkers, and environmental exposure data. The role spans infancy to adolescence, with external validation across cohorts.

You will publish in ML and clinical/respiratory venues, work with clinicians and data scientists, and contribute to reproducible code and open science practices. The position starts November 2026 or upon agreement.

Qualifications

• MSc in a quantitative field is required or explicitly stated in the posting.
• Strong programming skills in Python and experience with ML frameworks are mandatory.
• Experience with clinical or epidemiological data and small-sample studies is a strong plus.

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, focusing 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.
• Collaborate with clinicians, epidemiologists, and exposure scientists; contribute to reproducible, well-documented code.

Skills

Python

PyTorch

scikit-learn

statistical modelling

longitudinal data handling

model validation

data integration

English fluency

Education

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

Job description

University of Basel seeks a PhD student to lead machine learning for a pediatric lung health project, integrating clinical data, breath metabolomics, biomarkers, and environmental exposure data. The role spans infancy to adolescence, with external validation across cohorts.

You will publish in ML and clinical/respiratory venues, work with clinicians and data scientists, and contribute to reproducible code and open science practices. The position starts November 2026 or upon agreement.