Postdoctoral Research Position in Applied Machine Learning for Digital Health and Digital In-Vehicle Biomarkers

Building on the success of our research into digital biomarkers for detecting intoxicated driving, we are thrilled to announce a new CDHI Core research group: Digital Biomarkers in Driver Health and Safety. Led by Prof. Dr. Felix Wortmann and Prof. Dr. Elgar Fleisch, this core aims to develop and evaluate a robust cannabis-driving detection model before and while driving.

To support this core, we are actively recruiting for a postdoctoral researcher:

Over the last few decades, the prevalence of drug-induced fatal car accidents has drastically increased. In the US, for example, the National Highway Traffic Safety Administration (NHTSA) reports that 56% of the injured or killed roadway participants tested positive. Thereby, the most prevalent drug was cannabis with 25%. Since the legalization of cannabis is currently globally progressing, urgent measures are needed to prevent a highly likely increase of such accidents.

Project background

Considering these challenges, we are looking for a personality with a strong will to create a meaningful impact. We aim to design, implement, and evaluate a cannabis-impaired driving detection and prevention system within the Revelio initiative. Building upon previous studies that have been well-recognized for their contributions (ETH medal, CHI Best Paper Award), we will conduct a real-world study in which subjects are put under the influence of cannabis while performing a pre-driving test and a driving task on a test track. We then develop and evaluate a robust cannabis-driving detection model before and while driving. Ultimately, we are working towards a scalable digital biomarker platform for in-vehicle driver state detection, contributing to the long-term goal of Vision Zero (eliminating traffic-related fatalities and severe injuries).

Job description

The work will contribute to the broader field of digital health. Accordingly, we offer the position of a Postdoctoral Researcher at the Department of Management, Technology, and Economics (MTEC) at ETH Zürich within the Centre for Digital Health Interventions. You will lead the Revelio initiative and co-supervise two PhD students in close collaboration with Prof. Elgar Fleisch (ETH Zürich) and Prof. Felix Wortmann (University of St. Gallen). The project is funded for 3 years by the Swiss Road Safety Fund (Fonds für Verkehrssicherheit) and will be conducted in close collaboration with Prof. Wolfgang Weinmann (Forensic Toxicology, University of Bern). The position is set to begin on July 1st, 2025, with flexibility regarding the start date. ETH Zürich offers a highly competitive salary and outstanding working conditions.

As part of an interdisciplinary team at the intersection of applied machine learning, behavioral science, forensic toxicology, and automotive safety, you will contribute to developing robust, real-time models for detecting and preventing impaired driving. Your work will be key in integrating novel in-vehicle sensing approaches and AI-driven interventions into next-generation automotive systems. To ensure real-world impact, your research must address critical challenges such as model generalization, adversarial robustness, and regulatory compliance, paving the way for adoption by automotive manufacturers and stakeholders, including Swiss and international road safety authorities.

You will also engage in promotional activities to increase community awareness of your work through conferences, workshops, keynotes, seminars, and social media engagement, while also working on grant proposals, study protocols and publications for high-quality, peer-reviewed outlets (e.g., ACM Conference on Human Factors in Computing Systems, Lancet Digital Health, npj Digital Medicine). You will also be involved in teaching activities.

Profile

We are looking for candidates with the following qualifications:

A PhD in Computer Science, Information Technology, Information Systems, Statistics, Data Science, Engineering, or a related field
Expertise in machine learning and (health) interventions (preferred)
Strong interest in interdisciplinary collaborations; prior experience is a plus
Strong interest in applied science
Familiarity with ethical, legal, and health-political challenges of interventional research
Strong verbal and written communication skills in English

For more information and to apply, please refer to the job posting.