A World-Class Research Environment at the Intersection of Sleep, Physiology and Health Technology
The Neural Control of Movement Lab at ETH Zurich, Department of Health Sciences and Technology, invites exceptional candidates to apply for a PhD position focused on the physiological links between sleep, stress, arousal and health in humans. The project combines mechanistic laboratory experiments with real-world, home-based and decentralized phenotyping, using multimodal physiological recordings, mobile health technologies and computational analysis pipelines.
The Neural Control of Movement Lab focuses on understanding how the brain controls behavior and on developing non-invasive interfaces to measure and modulate brain function during wake and sleep. Within this environment, the PhD student will contribute to a translational research program that aims to understand how daily stress and arousal states shape sleep physiology, recovery and health-relevant outcomes in real life.
The Department of Health Sciences and Technology at ETH Zurich unites researchers across neuroscience, biomedical engineering, movement science, nutrition, physiology and medicine. It offers an interdisciplinary environment for research at the interface of human biology, technology and clinical translation.
Sleep, stress and arousal are deeply interconnected physiological processes. Persistent stress and heightened arousal can alter the transition into sleep, change nocturnal brain and autonomic activity, and influence recovery, resilience and longer-term health. Yet many key mechanisms are still studied either in tightly controlled laboratory settings or through simplified real-world measures that do not capture the richness of human physiology.
This project addresses that gap by combining mechanistic in-lab studies with real-world phenotyping of sleep, stress and arousal in daily life. The goal is to develop rigorous, scalable research workflows that can measure how stress and arousal unfold across the day, how they affect sleep and overnight recovery, and which physiological signatures are most informative for health-relevant phenotypes.
The project will use multimodal biosignal acquisition, such as pupillometry, sleep EEG, ECG, respiration, wearable and actigraphy data, smartphone-supported assessments and validated questionnaires, together with advanced signal processing, statistical modelling and machine learning. The PhD student will help implement, coordinate and analyze studies that bridge controlled physiology and ecological, home-based assessment.
The successful candidate will design, conduct and analyze human studies linking sleep, stress, arousal and autonomic physiology in both laboratory and real-world settings. A central part of the position will be the implementation of robust research pipelines for mobile and decentralized phenotyping, including data collection workflows, biosignal processing, quality control, synchronization, feature extraction and computational analysis.
The position is ideal for a candidate who wants to combine hands-on human physiology, sleep research, mobile health methods and strong computational data analysis. Professional software engineers may support parts of the technical backbone, but the PhD student should be able to implement and understand the data flow, work with technical collaborators, implement research-facing pipelines and take scientific ownership of the physiological data and its interpretation.
Your work will include:
- Design and conduct mechanistic laboratory studies and real-world sleep, stress and arousal studies in human participants
- Acquire and manage multimodal physiological data during wake and sleep such as pupillometry, EEG, ECG, respiration, photoplethysmography, actigraphy, wearable sensor data, smartphone-based assessments and questionnaires
- Coordinate decentralized and home-based study workflows, including participant instructions, device logistics, remote monitoring, troubleshooting, documentation and data quality control
- Develop and maintain reproducible analysis pipelines for physiological and behavioral data, including preprocessing, synchronization, artifact handling, feature extraction, visualization and reporting
- Apply statistical modelling, signal processing and machine learning approaches to identify phenotypes of sleep, stress, arousal and recovery
- Interpret complex physiological data in relation to mechanisms of arousal regulation, autonomic control, sleep physiology and health-relevant outcomes
- Work closely with researchers, engineers, clinicians, students and external collaborators to connect experimental design, data infrastructure and scientific analysis
- Disseminate findings through scientific publications, conference presentations and contributions to larger translational research initiatives
This interdisciplinary project offers a rare opportunity to build expertise across human sleep physiology, stress and arousal science, mobile health, digital phenotyping and computational analysis. Hands-on data collection and rigorous data analysis will both be central components of the position.
We are looking for an outstanding candidate with a strong physiological and computational profile.
- A Master's degree (or near completion), in biomedical engineering, medical engineering, medical informatics, computational neuroscience, data science, or a closely related field, with demonstrated quantitative and programming skills
- Strong programming skills in Python, MATLAB, R, or comparable languages are essential, together with experience writing clean, well-documented, and reproducible analysis code
- Strong skills in data analysis, statistics, signal processing, computational modelling, machine learning or artificial intelligence for complex physiological or behavioral data
- Experience with physiological data acquisition and analysis, ideally including sleep EEG, ECG, respiration, photoplethysmography, actigraphy, wearable sensors or other multimodal biosignal time series
- A solid understanding of human physiology and strong motivation to work on sleep, stress, arousal, autonomic regulation, recovery and health-relevant phenotyping
- Experience designing or conducting human research studies, including experimental protocols, participant-facing work, ethics-aware procedures, documentation and quality control
- Ability to build and manage structured research pipelines, including data ingestion, preprocessing, synchronization, artifact handling, feature extraction, validation, visualization and reporting
- Capacity to coordinate real-world, home-based or decentralized studies with high reliability, including device workflows, participant instructions, troubleshooting and data integrity checks
- A highly structured, detail-oriented and reliable working style, with excellent documentation habits, careful follow-through and respect for sensitive human research data
- Scientific curiosity, intellectual independence, strong work ethic, problem-solving ability and enthusiasm for interdisciplinary research at the interface of physiology, technology and health
- Excellent communication skills and the ability to collaborate with researchers, engineers, clinicians, students, study staff, technical partners and human participants
- Fluency in English
- German language skills are an advantage, especially for participant-facing work
A fully funded PhD position for approximately four years in an excellent scientific and social environment at a world-leading university. The project combines human experimental work, real-world physiological data collection, computational analysis and translational research. It offers an outstanding opportunity to develop a distinctive scientific profile at the interface of sleep physiology, stress and arousal science, mobile health and digital phenotyping.
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. More than 30,000 people from over 120 countries find ETH Zurich to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet connected all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.
We look forward to receiving your online application until the 26.07.2026 with the following documents:
- Curriculum vitae, including educational history, publications, awards, technical skills, programming languages, software tools, data-analysis methods and experience with physiological or human participant data
- Tailored research motivation statement of max. two A4 pages, please explain specifically how your background fits this position, including your strongest computational or programming experience, your experience with human physiology or neuroscience research, one technically or scientifically challenging project you contributed to, and why you are interested in this specific PhD project
- Diplomas and transcripts
- Names, e-mail addresses and phone numbers of at least two references, mentioning the professional relationship for each reference
Optional: links to a GitHub profile, code repository, technical portfolio, thesis, pre- , publication or other work sample that demonstrates computational or data-analysis skills. Please do not include confidential or sensitive data
Further information about the Neural Control of Movement Lab, can be found on our Website. Questions regarding the position should be directed to Dr. Caroline Lustenberger, [email protected] (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
