Postdoctoral Researcher in Machine Learning & Pediatric Health (80-100%) University of Basel in Switzerland

The Department of Biomedical Engineering (DBE) at the University of Basel is seeking a Postdoctoral Researcher for the Analytics & Informatics for Child Health (AICH) group, embedded within the Basel Research Center for Child Health (BRCCH). This group is at the forefront of developing AI and machine learning methods, digital tools, and secure data pipelines to advance pediatric healthcare. The research environment is highly interdisciplinary, combining clinical medicine, machine learning, and data-intensive research, with close collaborations involving hospitals, clinical IT teams, and research partners at the University of Basel.

The successful candidate will lead and contribute to ML/AI research projects and publications, working alongside PhD students and other postdocs. Responsibilities include supporting junior researchers with data preparation, prototyping, and code organization, as well as building and managing containerized environments (Docker, Singularity) and orchestrating jobs via SLURM. The role also involves contributing to MLOps infrastructure, including experiment tracking, logging, and reproducible setups.

In addition to research, the postdoctoral researcher will handle IT administration for the group, maintaining local IT infrastructure and coordinating with UniBasel HPC and DBE IT services. Tasks include developing and maintaining data pipelines for clinical data ingestion, pseudonymization, and secure transfer to cluster environments, ensuring compliance with data security requirements. The researcher will also manage group website updates, support digital communication tools, and oversee software licensing, hardware procurement, and inventory.

Applicants must have a PhD in Computer Science, Machine Learning, Biomedical Engineering, or a related field, with a strong publication record in ML/AI. Essential skills include programming in Python, experience with ML frameworks (PyTorch, TensorFlow), workflow tools (MLFlow, W&B), and HPC or cloud compute environments. Interest in clinical or health data applications and experience with sensitive data handling are advantageous. Candidates should be highly independent, proactive, and comfortable working in interdisciplinary teams. Fluency in English is required; German is an advantage but not mandatory.

The position offers a research-focused role at the interface of machine learning, clinical medicine, and digital health, within a collaborative and supportive team culture. The University of Basel provides access to state-of-the-art HPC infrastructure, flexible working arrangements, and opportunities for career development in ML research, research engineering, and academic leadership. Employment conditions are in accordance with university standards.

Applications should include a CV, cover letter, and contact information for 2–3 references, submitted as a single PDF. Optional links to GitHub or portfolio are welcome. Apply via the University of Basel application portal or email Prof. Dr. Ece Özkan Elsen directly. The application deadline is May 28, 2026.