Doctoral Student in Neuromotor Interfaces for Dexterous Robot Teleoperation (multimodal egocentric vision + EMG)

Develop signal-processing and machine-learning methods for multichannel EMG, including discrete and continuous decoding of hand and finger activity. Develop egocentric computer-vision methods for understanding hands, objects, hand–object interactions, contacts, affordances, and task state. Develop multimodal learning and reasoning methods that combine EMG with egocentric vision to infer motor intent and interaction context. Investigate reasoning over objects, actions, interaction sequences, and task state to resolve ambiguous motor signals and anticipate intended actions. Apply methods to dexterous robot teleoperation, including mapping human motor intent to robot manipulators and dexterous hands. Disseminate your findings in publications at top-tier venues and open-source research results and tools where appropriate. Present research findings at academic conferences, workshops, and seminars.

The Sensing, Interaction & Perception Lab invites applications for a fully funded PhD position at ETH Zürich at the intersection of robotics, wearable sensing, signal processing, machine learning, and human-computer interaction. ETH Zurich is one of the world’s leading universities specialising in science and technology, renowned for excellent education, cutting-edge fundamental research, and direct transfer of new knowledge into society.

• written and spoken fluency in English

• an excellent master's degree (MSc., M.Eng. or equivalent) in Computer Science, Electrical Engineering, Robotics, or related field

• A strong foundation in machine learning, including classification and regression, model evaluation, representation learning, and generalization

• A solid understanding of signals and time-series data (e.g., sampling, frequency, phase, noise, spectral representations, and filtering)

• Strong programming skills and experience implementing and quantitatively evaluating computational methods

• An interest in building real-time sensing and interactive systems, including experimentation with physical sensors

• A strong background in one or more of the following areas is particularly beneficial: Electrical engineering and signal processing, Robotics, Computer Vision, Machine learning, Interactive systems.