PhD student in Machine Learning-based Event Detection in Electromyography Signals (EMG for AR/VR/XR and muscle training/rehabilitation)

100%, Zurich, fixed-term

The Sensing, Interaction & Perception Lab  is looking for a PhD student in Human-Computer Interaction at ETH Zurich. Our research will be focused on event detection and fine-grained activity detection using electromyography sensors as input. Supplemental input modalities will include inertial sensors (IMU). The research will focus on machine learning-based signal processing and time series analysis to detect moments of interest in EMG and IMU sensor data.

Based on the new detection methods, we will design interaction techniques for online scenarios. This can be in the areas of Augmented Reality and Virtual Reality to control an interactive interface as well as in the medical domain for rehabilitation, monitoring, and exercise training.

Key requirements for your application

Following the high number of unrelated applications, here are some guidelines:

• We will only respond to applications for this role submitted through this platform. Applications through email will be ignored.
• Your motivation letter must relate to the specifics of this position, especially how your experience relates to EMG/EEG, signal processing, or similar.
• We won't be able to advance candidates to the interview if no experience with EMG/EOG/EEG is evident.

Project background

Electromyography (EMG) is a diagnostic technique used to assess the health of muscles and the nerve cells that control them. It involves recording and analyzing the electrical activity produced by skeletal muscles. EMG is recorded using sensors, typically surface electrodes placed on the skin over the muscles that detect the signals generated during muscle contraction. The data collected through EMG is essential for diagnosing conditions like muscular dystrophy, and neuromuscular disorders, and for monitoring muscle responses in various medical and research settings. In addition to its medical applications, EMG is increasingly utilized in human-computer interaction, particularly in developing sophisticated control systems for prosthetics, gaming, and interactive virtual environments. By interpreting the specific patterns of muscle activity, EMG provides a unique interface that can translate human intention into machine control.

Job description

We aim to enhance interaction capabilities in AR/VR and monitoring capabilities in interactive medical scenarios through the advanced analysis of electromyography (EMG) data. Therefore, the research in this PhD will focus on developing robust machine learning algorithms for signal processing and time series analysis. These algorithms are designed to detect specific events and detailed activities from the raw data provided by the multimodal sensors.

• Literature review of machine learning techniques on signal and time series analysis (specifically EMG), including Multivariate Time Series Analysis, Sequence Modeling, Dimensionality Reduction, Anomaly Detection, Temporal Pattern Recognition, Feature Extraction and Engineering, and Prediction Models.
• Method development for machine learning-based signal processing, including predictive coding, contrastive learning, augmentation approaches, and multimodal learning from auxiliary inputs.
• Review and further development of state-of-the-art efforts in active learning, transfer learning with user-specific finetuning, and online learning.
• Experiment design for validation methods, offline based on datasets and online for empirical validation.
• Present research findings at academic conferences and seminars, engaging with the wider scientific community for feedback and knowledge exchange.
• Collaborations with others in the research team to integrate developed methods into broader project objectives, contributing to shared goals and interdisciplinary learning and applications that involve end-users and patients.
• Later on: Explore practical applications of the methods in areas like AR, VR, and medical technology, leading to the creation of functional prototypes or models.

Your profile

ETH requirements:

• written and spoken fluency in English
• an excellent master's degree (MSc., M.Eng. or equivalent) in Computer Science, Electrical Engineering, Bioengineering, Robotics, or related

requirements for the position:

• strong interpersonal and communication skills
• experience in signal processing
• experience in machine learning for time series; highly beneficial: experience with online inference
• understanding of multimodal data processing
• understanding of EMG signals (or similar: EOG, ECG, EEG) and data coding
• optional: sensing/electrical engineering background to understand EMG signal acquisition
• optional but useful: experience with VR or AR, Unity/C#

Prior experience in conducting user evaluations is useful but not a must. Experience with interactive and real-time systems is also a plus.

Your workplace

Your workplace

We offer

We offer an exciting environment and team to study in and work with. Beyond the lab, ETH Zürich has several internationally recognized research groups dedicated to interactive systems, Human-Computer Interaction, AR/VR, health, and machine learning. In our research, we often collaborate with other groups and departments as well as with several other institutions and companies in Switzerland and abroad.

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Curious? So are we.

Please submit your complete application through the online application portal:

• motivation letter (≤ 1 page)
• curriculum vitae (PDF)
• university transcript of records (bachelor's and master's)
• short overview of your experiences with signal processing
  (classical and/or machine learning-based)
• short description of your experience with EMG signals (or similar: EOG, ECG, EEG), experiments, applications
• contact details of 1–2 academic referees
• a link to your GitHub profile and/or your portfolio/website

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Applications will be evaluated on a rolling basis. The position is available for as long as this job ad is up and the job ad will be deactivated when the position has been filled. We are looking to fill the position as soon as possible with a start date in Spring 2024.

If your questions are not answered in this post, please direct them to [email protected]  (please do not send applications via email).

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.