Scientific Assistant in Data Science for Spinal Cord Injury Research

Scientific Assistant in Data Science for Spinal Cord Injury Research
100%, Zurich, fixed-term

The Biomedical Data Science Lab (BDS Lab) at ETH Zurich is seeking a highly motivated Scientific Assistant to join our interdisciplinary research team working at the intersection of data science, neuroscience, and clinical research.

Project background

This position focuses on spinal cord injury (SCI) and aims to develop data-driven methods to improve clinical trial design, predict recovery trajectories, and identify factors influencing patient outcomes.

You will work under the supervision of Prof. Catherine Jutzeler, head of the Biomedical Data Science Lab, and in close collaboration with spinal cord injury specialists and clinical trial experts from ETH Zurich and partner institutions. Your work will directly contribute to ongoing efforts in clinical trial planning, including modeling of historical controls, as well as machine learning, data science, and epidemiological studies based on large SCI datasets. This is an excellent opportunity to contribute to translational research at the interface of computational modeling, biomedical data analysis, and clinical neuroscience.

Job description

• Support clinical trial planning, including modeling of historical control groups and the development of data-driven study design strategies
• Conduct machine learning, data science, and AI analyses on large-scale SCI datasets
• Perform epidemiological analyses to identify predictors of recovery, treatment response, and long-term outcomes
• Develop and maintain reproducible data analysis pipelines and workflows
• Perform data visualization and generate reports to support publications and collaborative projects
• Work closely with SCI clinicians, data scientists, and clinical trial experts from ETH Zurich and partner institutions
• Assist in the organization and participation of the annual lab retreat, contributing to scientific exchange and team development

Profile

• Master's degree or equivalent in Data Science, Computer Science, Biomedical Engineering, Neuroscience, or a related field
• Solid experience in Python and/or R, including common data analysis and machine learning libraries
• Knowledge of statistical modeling, epidemiology, or clinical trial methodology is a strong advantage
• Experience with biomedical or clinical data, such as neuroimaging, electrophysiology, or registry data
• Excellent analytical, organizational, and communication skills
• Strong motivation to contribute to translational neuroscience and evidence-based clinical research
• Ability to work both independently and collaboratively in a multidisciplinary environment

Workplace
Workplace
We offer

• A stimulating, collaborative research environment within ETH Zurich, one of the world's leading universities for science and technology
• The opportunity to contribute to cutting-edge biomedical data science with direct clinical relevance
• Close collaboration with spinal cord injury and clinical trial experts from ETH Zurich, the Swiss Paraplegic Centre and University Hospital Balgrist, as well as industry partners
• Access to modern computational infrastructure and mentorship under Prof. Catherine Jutzeler
• Possibilities for professional development and training in data analysis, machine learning, and neuroinformatics

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

Applications must be submitted via the ETH online portal. Applications sent via email will not be considered.

Please submit the following documents via the portal:

• Curriculum vitae
• Transcripts and certificates
• Contact information for two references

• Task-based statements: For each of the following tasks, provide a short description (maximum 1 page per task) of how you would approach the problem, including methods, tools, or analysis strategies you would use

• Historical control modeling for a clinical trial in the context of rare diseases — describe how you would construct and validate a model using historical data

• Machine learning / data science study on a large-scale SCI dataset using the EMSCI dataset — describe how you would approach data preprocessing, modeling, and evaluation

Applications will be reviewed on a rolling basis until the position is filled.

For further information, please contact Prof. Catherine Jutzeler at and Dr. Liliana Paredes ), and visit our website.

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