Scientific Assistant – Biomedical Data Science & Spinal Cord Injury Research

The Biomedical Data Science Lab investigates data-driven solutions for healthcare applications with a focus on neurological conditions and systemic infections such as sepsis. At the core of our research is the collaboration across disciplines spanning expertise in medicine, biology, computer, and data science. We seek a motivated scientific assistant to join this growing team and contribute to interdisciplinary research partnerships.

The Biomedical Data Science Lab at ETH Zurich invites applications for a Scientific Assistant to support our data-intensive projects in biomedical imaging and clinical research. This position offers an excellent opportunity to work at the forefront of biomedical data science, contributing to key projects including data curation, image segmentation, and tool development for clinical research applications. The scientific assistant will support the Biomedical Data Science Team for a range of application projects, with a particular focus on our work in pediatric sepsis, lower back pain, and pediatric neurooncology.

We are looking for a Scientific Assistant with a strong background in statistics, machine learning, and biomedical data science to support our research using the European Multicenter Study about Spinal Cord Injury (EMSCI) and related SCI databases.

The position combines hands-on data analysis and scientific project support with the coordination of scientific activities within our research network. The successful candidate will work closely with researchers, clinicians, and international collaborators to analyse large-scale longitudinal SCI datasets and contribute to scientific publications and meetings.

Your key responsibilities will include:

• Conduct statistical and machine-learning analyses using EMSCI and related SCI datasets.
• Develop and validate predictive models of neurological recovery and functional outcomes after SCI.
• Perform data preprocessing, quality control, harmonisation, feature engineering, and exploratory data analysis.
• Apply appropriate statistical methods, including regression, longitudinal data analysis, survival analysis, and machine-learning approaches.
• Develop reproducible analytical workflows in R and/or Python.
• Contribute to scientific publications, reports, presentations, and grant applications.
• Collaborate with clinicians, neuroscientists, statisticians, and data scientists across participating SCI centres.
• Support the organisation and coordination of scientific meetings, workshops, consortium meetings, and other research activities, including scheduling, preparation of agendas and materials, communication with participants, and follow-up.
• Assist with the coordination of ongoing collaborative projects and follow-up of scientific activities and deliverables.

• MSc or equivalent degree in statistics, biostatistics, data science, biomedical engineering, computer science, epidemiology, neuroscience, or a related discipline.
• Strong background in statistics and/or machine learning.
• Experience working with longitudinal and/or clinical datasets.
• Strong programming skills in R and/or Python.
• Experience with statistical modelling, data visualisation, and reproducible research.
• Familiarity with machine-learning methods such as random forests, gradient boosting, neural networks, or related approaches.
• Previous experience working with EMSCI data or other large SCI databases is highly desirable.
• Knowledge of SCI-specific clinical and neurological outcome measures (e.g., ISNCSCI/ASIA, SCIM) is an advantage.
• Experience working with multicentre clinical datasets is an advantage.
• Fluent in English and proficient in German, both written and spoken.
• Excellent organisational and communication skills.
• Ability to work independently and reliably while collaborating effectively in a multidisciplinary and international research environment.
• Interest in SCI research and the application of data science to clinical research.

• An 80–100% position within the Biomedical Data Science Lab at ETH Zurich.
• The opportunity to work with EMSCI, one of the major longitudinal SCI research databases, and related international datasets.
• A highly interdisciplinary environment at the interface of clinical neuroscience, statistics, machine learning, and biomedical data science.
• Close interaction with leading SCI researchers and clinical centres in Switzerland and internationally.
• The opportunity to contribute to high-quality scientific publications and international collaborative projects.
• A varied role combining hands-on quantitative research with scientific coordination and project management.
• A stimulating research environment at ETH Zurich, with the lab based in Schlieren.

Working, teaching and research at ETH Zurich

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