Biomedical Data Science Lab: Scientific Assistant Position
The Biomedical Data Science (BMDS) Lab is dedicated to discovering data-driven solutions for healthcare applications, focusing on neurological conditions such as spinal cord injury (SCI), lower back pain, neurodegenerative disorders, and neurological tumors. Our research thrives on interdisciplinary collaboration, integrating expertise from medicine, biology, computer science, and data science. We are currently seeking a Scientific Assistant to join our expanding team and contribute to innovative research partnerships. The anticipated start date for this position is March 1, 2026.
Project Overview
Traumatic spinal cord injury (SCI) has profound and lifelong consequences for affected individuals and their families. A significant challenge in predicting long-term recovery lies in the substantial heterogeneity of patient outcomes, which traditional clinical assessments may not fully capture.
Standard neurological evaluations alone cannot reflect the underlying biological and functional diversity, imposing limitations on prediction accuracy and the effectiveness of treatment strategies. Our project aims to bridge this gap by integrating neurological assessments with neurophysiological measurements and routine blood biomarkers. By leveraging advanced machine learning techniques to combine these diverse data sources, we strive to identify the most informative clinical features, thereby providing more accurate and interpretable recovery predictions. This findings will support improved patient stratification, prognosis, and personalized treatment strategies.
Key Responsibilities
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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 will be readily available at the start of the project.
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Develop Advanced Deep Learning Models: Design and implement a multi-branch neural network capable of integrating multiple data modalities into a unified representation.
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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.
Qualifications
- You hold a Master's degree in Computer Science, Data Science, Biomedical Engineering, or a related field.
- You possess strong Python programming skills and have a proven track record in developing and training machine and deep learning models using Keras/TensorFlow and/or PyTorch, along with experience in statistical data analysis.
- You have experience with collaborative coding practices, version control (e.g., Git), and working on computing clusters.
- Experience with SCI data or related research topics is an advantage.
- A background in biomedical projects and experience in interdisciplinary collaboration is a plus.
- You are motivated to work as part of a diverse team and are committed to scientific excellence in your field.
- You are proficient in both written and spoken English.
What We Offer
- A 1-year project-based contract at the BMDS Lab (80% workload).
- A stimulating, collaborative 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.
- The chance to enhance your skills in data science, machine learning, and neuroinformatics applied to critical health conditions, with a focus on SCI.
- Be part of a highly motivated, multidisciplinary, and collaborative team.
- Learn from field experts and contribute to an active research lab.
Application Process
To apply for this position, please apply online using the form below. Only applications matching the job profile will be considered.
Further Information
For additional information about the BMDS Lab, please visit our website. If you have any questions regarding the position, please reach out to Dr. Olga Taran via email at olga.taran@hest.ethz.ch (no applications).