Doctoral Student in Neuromotor Interfaces for Robot Teleoperation
Develop multimodal neuromotor interfaces combining wearable sEMG and egocentric vision for dexterous robot teleoperation at ETH Zürich.
Best for:Best for machine learning engineers and robotics researchers with a strong background in signal processing, computer vision, and wearable systems.
Overview
The Sensing, Interaction & Perception Lab at ETH Zürich invites applications for a fully funded PhD position focused on the intersection of robotics, wearable sensing, signal processing, and machine learning. The project centers on designing advanced neuromotor interfaces for dexterous robot teleoperation by integrating surface electromyography with egocentric vision. The successful candidate will build computational and sensing methods to decode human intent and environmental context in real time. The appointee will conduct ambitious research, collaborate with leading research hubs, and publish findings at premier international venues.
What you'll do
- Develop signal-processing and machine-learning methods for multichannel surface electromyography to decode hand and finger activity.
- Build egocentric computer-vision techniques for understanding hands, objects, and interaction states.
- Design multimodal learning and reasoning frameworks that combine EMG signals with egocentric video input.
- Investigate reasoning over action sequences and task states to resolve ambiguous motor intents.
- Apply developed methods to real-time dexterous robot teleoperation and mapping of human intent to robotic manipulators.
- Disseminate research outcomes through peer-reviewed publications at top-tier academic venues and open-source tools.
Requirements
Degree required: Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field
Degree offered: PhD
Language: English; written and spoken fluency
Instruction: English
- Strong foundation in machine learning including classification, regression, representation learning, and generalization
- Solid understanding of signals and time-series data analysis such as filtering, sampling, and spectral representations
- Strong programming skills and experience implementing and evaluating computational methods
- Demonstrated interest in building real-time sensing and interactive physical systems
Funding & terms
How to apply
Prepare a complete application package including a motivation letter, CV, academic transcripts, and reference contact details. Submit all required files through the online application portal.
- Motivation letter
- CV
- University transcript of records
- Reference contact details
Contact
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