Scientific Assistant – Biomedical Data Science & Spinal Cord Injury Research

About the role

The Biomedical Data Science Lab at ETH Zurich, in Schlieren, is looking for a Scientific Assistant to support its data-intensive projects in biomedical imaging and clinical research, with a focus on neurological conditions and sepsis. The position supports the Biomedical Data Science Team on projects in pediatric sepsis, lower back pain and pediatric neurooncology, and works with the European Multicenter Study about Spinal Cord Injury (EMSCI) and related SCI databases. It is an 80-100% fixed-term position of 1 year, with a possible extension of 1 year, starting preferably on 1 November 2026.

Key responsibilities

• Analyse large-scale longitudinal SCI datasets with statistical and machine-learning methods, including regression, longitudinal analysis, survival analysis and machine learning.
• Develop and validate predictive models of neurological recovery and functional outcomes after spinal cord injury.
• Perform data preprocessing, quality control, harmonisation, feature engineering and exploratory data analysis.
• Build reproducible analytical workflows in R and/or Python.
• Contribute to publications, reports, presentations and grant applications.
• Coordinate scientific activities within the research network, including meetings, workshops and consortium meetings, and follow up collaborative projects.

Required profile

• MSc or equivalent in statistics, biostatistics, data science, biomedical engineering, computer science, epidemiology, neuroscience or a related field.
• Strong background in statistics and/or machine learning, and experience with longitudinal or clinical datasets.
• Fluent English and proficient German, written and spoken.
• Interest in SCI research and applying data science to clinical research.

Required skills

• Strong programming in R and/or Python.
• Statistical modelling, data visualisation and reproducible research.
• Familiarity with random forests, gradient boosting, neural networks or similar methods.
• Knowledge of SCI outcome measures (ISNCSCI/ASIA, SCIM) and multicentre clinical datasets is an advantage.

Applications are submitted online through the ETH Zurich application portal with a CV, a short application video answering a research question about EMSCI data, a cover letter, employment references and diplomas. The video should be a personal recording of about three minutes.