Scientific Assistant in Biomedical Machine Learning and Data Science

Scientific Assistant in Biomedical Machine Learning and Data Science

The Biomedical Data Science (BMDS) Lab investigates data-driven solutions for healthcare applications with a focus on neurological conditions such as spinal cord injury (SCI), lower back pain, neuro-degenerative disorders and neurological tumors. At the core of our research is the collaboration across disciplines spanning expertise in medicine, biology, computer and data science. We are seeking a scientific assistant to join this growing team and contribute to interdisciplinary research partnerships. The anticipated start date is March 1, 2026.

Project background

Traumatic spinal cord injury (SCI) has profound and lifelong consequences for affected individuals and their families. A major challenge in predicting long-term recovery is the substantial heterogeneity in patient outcomes, which traditional clinical assessments might not fully capture. Standard neurological evaluations alone cannot reflect the underlying biological and functional diversity, limiting both prediction accuracy and the effectiveness of treatment strategies. This project aims to address this gap by integrating neurological assessments with neurophysiological measurements and routine blood biomarkers, leveraging advanced machine learning to combine these diverse data sources. By identifying the most informative clinical features, the approach seeks to provide more accurate and interpretable recovery predictions, supporting better patient stratification, prognosis, and personalized treatment strategies.

Job description

• Explore and manage SCI datasets : Work with international databases of SCI patient data, ensuring accurate handling, preprocessing, and integration of heterogeneous clinical, neurophysiological, and biomarker information. These datasets are readily available at the start of the project.
• Develop advanced deep learning models : Design and implement a multi-branch neural network capable of integrating multiple data modalities into a unified representation.
• Implement a multi-task learning framework : Build and evaluate models that predict multiple, related recovery outcomes simultaneously, leveraging shared information between tasks to enhance predictive performance, interpretability, and generalization.

Profile

We offer

We offer a 1-year project-based contract at the BMDS Lab ( 80% workload ).

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.

We look forward to receiving your online application with the following documents :