PhD Position in Machine Learning for Predicting States of Receptivity in Digital Therapeutics

Project background

Healthcare systems worldwide face challenges arising from the increase in non-communicable diseases (NCDs), such as cardiovascular disease or mental disorders, their related risk factors, and associated economic costs. A holistic intervention paradigm focusing on physical, mental, and social health is needed to prevent and manage NCDs effectively. However, current interventions are limited in scalability and do not provide data-driven, precise, and actionable interventions. To this end, the Mobile Health Interventions module of the FHT program investigates how ubiquitous technology can be leveraged to support individuals at risk in the most scalable way. This is done by detecting vulnerable states and delivering high precision and actionable interventions, for example, with the help of digital biomarkers, smartphones, wearables, chatbots, or voice assistants.

Job description

As a PhD student, you will make prevention measurable, actionable, and accountable by developing effective digital therapeutics for healthy longevity. In particular, you will use intensive longitudinal behavioural and physiological data (e.g., smartphone sensor data streams) and work on a smartphone-based digital therapeutic that predicts states of receptivity, i.e., opportune moments when individuals are able to receive, process, and use support. To this end, your research will build upon our award-winning work (ACM IMWUT Distinguished Paper Award 2022) on states of receptivity (Kuenzler et al. 2019 and Mishra et al 2021). The resulting state of receptivity system will be used to trigger behavioural precision interventions that slow down biological ageing processes. MobileCoach ( written in Java and Javascript (ReactNative), among other technologies, will be used for your thesis project.

You will work in a highly interdisciplinary team at the intersection of computer science, behavioural medicine, clinical psychology, and business innovation. You will also engage in promotional activities to increase community awareness of your digital biomarker research through conferences, workshops, keynotes, seminars, and social media engagement, while also working on grant proposals, study protocols and publications for high-quality, peer-reviewed journals (e.g., Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)Lancet Digital Healthnpj Digital MedicineDigital Biomarkers).

Your profile

You will have:

  • A Master’s degree in computer science, machine learning, statistics or related fields
  • Programming experience
  • Positive experiences with interdisciplinary collaborations
  • Strong verbal and written communication skills in English
  • Scientific curiosity and motivation to perform scientifically rigorous experimental work

The following competence will be advantageous:

  • Familiarity with Java, Javascript (ReactNative), and mobile app programming (iOS and Android)
  • Familiarity with ethical, legal, and health-political challenges of medical research

We offer

  • a PhD position that allows you to contribute to the ongoing health challenges of our society
  • an interdisciplinary team of passionate researchers working at the intersection of digital health technology and disease prevention
  • exciting professional development opportunities
  • access to a global network of digital health enthusiasts
  • opportunities to present your research to local and international audiences

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We value diversity

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.

Curious? So are we.

We look forward to receiving your online application including the following documents:

  • Cover letter outlining your motivation and experience in the field
  • CV including certificates (e.g. Master’s and/or Bachelor’s degree)
  • An example of academic writing (e.g., your Master’s thesis).
  • List of data science projects with code examples (e.g. link to a code repository)
  • Transcript of records

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

For further information about our research and projects, please visit our website. More information about the Mobile Health Intervention Module of the FHT programme is available here: Questions regarding the position should be directed to Prof. Dr Tobias Kowatsch at (no applications).