PhD Position Movement Biomarkers in Stroke Recovery

PhD Position Movement Biomarkers in Stroke Recovery 100%, Zurich, fixed-term

The Lake Lucerne Institute (LLUI) is an independent, non-profit research and education institute based in Vitznau, Switzerland, committed to developing and implementing technological solutions that improve rehabilitation outcomes and bridge the gap between theory and practice. Within the Therapy Science Lab, our research focuses on ubiquitous rehabilitation—aligning interventions with a patient’s clinical state, lesion characteristics, and multimodal biomarkers to optimize therapeutic effectiveness, across the pathway of care.

This research axis is embedded in the CEREBRIS project, an EIC Pathfinder Open initiative conducted by a consortium of 14 European partners. We are seeking a highly motivated PhD student interested in research in upper limb marker less motion capture, movement classification, outcome modelling in stroke populations, and synthetic data evaluation. The position offers the opportunity to advance and deploy scalable biomechanical assessment systems in clinical and research environments, directly contributing to patient-centered rehabilitation planning and service optimization.

Beyond strengthening your scientific and methodological expertise, you will gain experience in clinically grounded research that connects neurorehabilitation, movement science, and data-driven approaches. By project completion, the broader CEREBRIS technology is expected to reach Technology Readiness Level 4, corresponding to validated laboratory demonstration.

The CEREBRIS project aims to establish a novel, data-driven framework for the management of neurological diseases, with an initial focus on stroke. By integrating advanced artificial intelligence methodologies with clinically meaningful digital biomarkers, the consortium seeks to transform the stroke care pathway—from early diagnosis and targeted treatment decisions to continuous monitoring and outcome prediction. The project encompasses the development of several interlinked applications including an automated motor function interface utilizing marker less 3D motion capture (Axo Motris).

CEREBRIS offers the opportunity to contribute to an interdisciplinary European research project at the interface of neurorehabilitation, movement analysis, multimodal data, and AI-supported stroke care. Within the Axo Motris part of CEREBRIS, the PhD candidate will focus on identifying movement biomarkers relevant to stroke recovery using clinical and sensor-based data. The position provides exposure to multimodal clinical and laboratory-based data acquisition, collaboration with computer scientists, engineers, clinicians, and translational researchers across Europe, and insight into how digital health technologies are developed, evaluated, and prepared for clinical translation.

Your responsibilities will include:

• Upper-limb marker less kinematics (50%) – Contribute to development and validation of multi-view (primary) and monocular (secondary) marker less motion capture pipelines, and a quality assurance framework for kinematic datasets.
• Clinical interpretable information (20%) – Assist in translating kinematic outputs into clinician-interpretable formats.
• Scientific writing and dissemination (20%)
• Synthetic data (10%) – Support generation and implementation of synthetic data.

We are looking for a motivated and committed candidate who brings:

Mandatory skills

• Experience with computer vision for motion analysis
• Master’s degree in Biomechanics, Biomedical Engineering, Computer Vision, Computer Science, Neuroscience, Health Science, Biomedicine, Physio- or Occupational Therapy, or a related field.
• Understanding of study design, scientific methods, and statistical modelling
• Python, C# or equivalent language expertise
• Basic knowledge of machine learning or deep learning

Desirable Skills

• Exposure to musculoskeletal modelling
• Familiarity with containerization or version control workflows
• Interest in regulatory AI reporting frameworks
• Interest in synthetic data generation
• Personal Profile
• Reliability and structured work
• Strong communication skills in English (oral and…