Professor of Biosensing Prevention and Rehabilitation

Job Overview

Department of Health Sciences and Technology ((Use the "Apply for this Job" box below).) at ETH Zurich invites applications for the following position.

The new professorship will focus on the development of new diagnostic and prognostic methods (biosensors, biomarker analytics, artificial intelligence). These methods are expected to be validated via integration into clinical workflows. The role will contribute to digital/personalized health by improving prevention and therapies in prehabilitation and rehabilitation. Fields of application include oncology, geriatrics, neurology, paraplegiology, and surgery. The research aims to develop biological, chemical, molecular, optical, electrical, or other sensing technologies for early diagnosis of potential pre‑ and postoperative complications and for health monitoring.

The professorship will also address telehealth systems for remote clinical services, wireless medical devices as decision‑support systems, and advanced data analytics (digital twins and AI).

The professorship will be established at ETH Zurich with premises mainly at EMPA, St. Gallen, and in conjunction with clinical research at the Kantonsspital St. Gallen to develop patient‑specific diagnostic tools and accelerate clinical translation of research results. The successful candidate will oversee the translation of materials, technologies, protocols, and AI algorithms to clinics and industrial partners. Experience in or a strong link to clinical medicine is desired.

Expected contributions include teaching at the undergraduate level (German or English) and at the graduate level (English). Engagement in practical teaching such as focus projects or teaching labs is also anticipated.

Ideal candidates have a background in engineering with specialization in biomechatronics, biochemistry, biomaterials, biomolecular analytics, or biostatistics, enabling them to bridge basic research and clinical application across scales from molecular to human levels. Expertise in biomedical engineering, especially measurement sciences such as biosensing technologies, biochemical analysis, molecular diagnosis, advanced materials for biosensing, or advanced data science techniques, is considered an asset.