ETH Zürich
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Doctoral Student in Neuromotor Interfaces for Dexterous Robot Teleoperation (multimodal egocentric vision + EMG) ETH Zürich in Switzerland
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Country
Switzerland
University
ETH Zürich

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About this position
ETH Zürich is offering a fully funded PhD position in the Sensing, Interaction & Perception Lab for research at the intersection of robotics, wearable sensing, signal processing, machine learning, computer vision, and human-computer interaction.
The project focuses on neuromotor interfaces for dexterous robot teleoperation using two complementary sensing modalities: surface electromyography (sEMG) measured at the wrist or forearm, and egocentric vision. The goal is to decode subtle hand and finger activity, continuous movement, and motor intent in real time, while also using first-person video to understand the surrounding scene, manipulated objects, hand-object interactions, affordances, contacts, and task state.
Research topics include multichannel EMG signal processing, temporal and multimodal machine learning, representation learning, adaptation, real-time inference, egocentric computer vision, and reasoning across neuromotor and visual signals. The work is aimed at robust and generalizable systems that can operate in realistic settings and support applications in dexterous manipulation, shared autonomy, multimodal intent inference, Mixed Reality, and other interactive systems.
The role is well suited to candidates with a background in computer science, electrical engineering, robotics, or related fields, especially those interested in wearable sensing, robot learning, human-robot interaction, and multimodal perception. A strong master’s degree is required, along with fluent written and spoken English. The lab particularly welcomes applicants with experience in EMG or other biosignals, embedded systems, egocentric vision, hand/object understanding, temporal modeling, or interactive sensing systems.
ETH Zürich highlights a strong research environment, access to wearable and embedded prototyping infrastructure, egocentric sensing and robotic manipulation platforms, and opportunities to collaborate with the ETH AI Center and ETHAR, ETH’s research hub for augmented reality. The position emphasizes publication in top venues, open-source research where appropriate, and intellectually ambitious doctoral research.
Applications are submitted through the ETH online portal and are reviewed on a rolling basis until the position is filled. The earliest start is Fall 2026. Required application materials include a one-page motivation letter, CV, transcripts for bachelor’s and master’s studies, and contact details for 1–2 academic references. A GitHub profile or portfolio/website may also be included.
For questions, the contact email listed is [email protected], though applicants are explicitly asked not to email to inquire about the status of the position.
Funding details
Available
What's required
Applicants must have written and spoken fluency in English and an excellent master's degree (MSc., M.Eng. or equivalent) in Computer Science, Electrical Engineering, Robotics, or a related field. Strong foundations are expected in machine learning, including classification, regression, model evaluation, representation learning, and generalization; time-series and signal processing concepts such as sampling, frequency, phase, noise, spectral representations, and filtering; and strong programming skills with experience implementing and quantitatively evaluating computational methods. An interest in real-time sensing and interactive systems, including experimentation with physical sensors, is important. Helpful background includes EMG or other electrophysiological signals, sensor systems, embedded systems, electronics, wearable sensing, robotics, manipulation, dexterous manipulation, teleoperation, robot learning, shared autonomy, human-robot interaction, egocentric vision, hand/object pose estimation, hand-object interaction, contact and affordance estimation, video understanding, action recognition, temporal modeling, multimodal learning, multimodal reasoning, domain adaptation, learning from noisy sensor data, HCI, Mixed Reality, wearable computing, or real-time input systems.
How to apply
Submit your application through the online portal. Include a motivation letter, CV, transcripts for bachelor's and master's studies, and contact details for 1–2 academic references. You may also add a GitHub profile or portfolio/website. Do not email to ask whether the position is still open; it is open until filled and reviewed on a rolling basis.
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