Universität Zürich · Education
Research Assistant,
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- Co-design and implement a VR testbed for FOG triggering, including the design and standardization of virtual scenarios (e.g., turning triggers, visual underload, and dual-task paradigms).
- Collaborate with clinicians to test and validate scenarios in clinical settings.
- Use biomarkers for FOG phenotyping by collecting and analyzing oculography, muscle activity (EMG), and motion data during VR-triggered FOG assessments.
- Integrate VR, oculography, EMG, and motion capture into a unified clinical assessment tool.
- Excellent Bachelor's or Master's degree (or equivalent) in Computer Science, Biomedical Engineering, or a closely related field.
- Strong programming skills in C#, Unity, and Python.
- Demonstrated experience in recording and analyzing multimodal sensor data.
- Strong written and verbal communication skills, with the ability to convey complex information clearly to both - expert and non-expert audiences.
- Excellent English communication skills; proficiency in German is an advantage.
- Prior experience in neuroscience, clinical research, and patient interaction is a plus.
- Strong analytical and problem-solving skills, combined with scientific rigor and intellectual curiosity to uncover meaningful insights.
- Creative, self-motivated, and able to thrive in a highly collaborative and interdisciplinary environment.