PhD Position Sleep, Stress and Arousal in Humans

PhD Position on Sleep, Stress and Arousal in Humans

A world‑class research environment at ETH Zurich, Department of Health Sciences and Technology, invites exceptional candidates for a four‑year PhD focused on the physiological links between sleep, stress, arousal and health in humans. The project combines mechanistic laboratory experiments with real‑world, home‑based phenotyping, multimodal physiological recordings, mobile health technologies and computational analysis pipelines.

Sleep, stress and arousal are deeply interconnected physiological processes. Persistent stress and heightened arousal alter sleep transitions, nocturnal brain and autonomic activity, and influence recovery and long‑term health. The project addresses this gap by combining in‑lab studies with real‑world phenotyping of sleep, stress and arousal in daily life. Using multimodal biosignals such as pupillometry, EEG, ECG, respiration, wearable and actigraphy data, smartphone assessments and validated questionnaires, the PhD student will implement, coordinate and analyze studies that bridge controlled physiology and ecological, home‑based assessment.

• 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 (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 behavioural data (preprocessing, synchronization, artifact handling, feature extraction, visualization and reporting).
• Apply statistical modelling, signal processing and machine learning 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.
• Collaborate 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.

• Master’s degree (or near completion) in biomedical engineering, medical engineering, medical informatics, computational neuroscience, data science or a closely related field with quantitative and programming skills.
• Strong programming skills in Python, MATLAB, R or comparable languages; experience writing clean, reproducible code.
• Experience in data analysis, statistics, signal processing, computational modelling, machine learning or AI for complex physiological or behavioural data.
• Experience with physiological data acquisition and analysis (sleep EEG, ECG, respiration, photoplethysmography, actigraphy, wearable sensors or other multimodal time series).
• 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 procedures, ethics‑aware procedures, documentation and quality control.
• Ability to build and manage structured research pipelines (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.
• Highly structured, detail‑oriented and reliable working style with excellent documentation habits.
• Scientific curiosity, intellectual independence, strong work ethic, problem‑solving ability and enthusiasm for interdisciplinary research at the interface of physiology, technology and health.
• Excel…