PhD Position (100%) LLM-Agentic Digital Therapeutics for Healthy Longevity in Middle-aged and Older Adults

We are more than happy to offer a fully funded PhD Position (100%, 3.5 years) at the University of St. Gallen (HSG) on LLM-Agentic Digital Therapeutics for Healthy Longevity in Middle-aged and Older Adults with Type 2 Diabetes (T2D) Prevention as use case. This PhD position in Management (Behavioral Science) is offered within the new Innosuisse Flagship project Swiss Precision Digital Therapeutics for the Prevention of Type 2 Diabetes. The successful candidate focuses on one of the most critical questions in modern healthcare systems: How to reach vulnerable individuals and deliver effective lifestyle interventions for disease prevention?

While T2D prevention serves as the primary empirical case, the research addresses a broader challenge: developing safe, scalable, and effective interventions for disease prevention and healthy longevity within the Swiss healthcare system.

Project Context

T2D is among the most prevalent non-communicable diseases in Switzerland, causing substantial health complications and long-term costs for individuals, insurers, and society. Although effective preventive interventions exist, they often fail due to limited long-term engagement, unclear reimbursement structures, and weak business models. Our Innosuisse Flagship project brings together a unique national consortium spanning digital health, wearable technologies, health insurers, food retailers, hospitals, public authorities, and innovation parks. Together, we aim to redesign diabetes prevention as a scalable, data-driven, and economically sustainable service embedded in real-world care and financing structures.

Your PhD Project

Developing an LLM-agentic digital therapeutic for healthy longevity in middle-aged and older adults at risk of developing type-2 diabetes

As a PhD student, you will work closely with another PhD student and software engineer to co-lead Subproject 4, which focuses on developing an LLM-agentic digital therapeutic for vulnerable populations (e.g. those with lower health literacy and/or socioeconomis status). Your research will involve identifying and designing a modular personalized lifestyle intervention, as well as real-world evaluation with middle-aged to older adults.

Key Research Focus

• Understanding the needs of middle-aged and older adults for T2D risk reduction.
• Co-designing a modular, personalized lifestyle intervention framework.
• Implementing a GenAI-powered Digital Health Companion.
• Predictive modelling and personalization optimization.
• Real-world evaluation of intervention effectiveness.

T2D prevention will be your core empirical context, while your theoretical contributions will be relevant across non-communicable diseases and healthy longevity initiatives.

Your Role and Responsibilities

We are seeking an outstanding PhD candidate to join our interdisciplinary team. The ideal candidate would have a strong technical background and understanding of conversational agents, chatbots, large language models, agentic frameworks, and just-in-time adaptive interventions. Furthermore, the candidate demonstrates a strong willingness to engage with concepts and methods from adjacent disciplines (e.g., behavioral medicine and cognitive science) and to contribute to a supportive, collaborative team environment.

Your Academic Environment

• Degree: PhD in Management (Behavioral Science)

• Institution: University of St. Gallen (HSG)

• Supervision:

  • Dr. Rasita Vinay, Institute for Implementation Science in Health Care (IfIS) & Institute of Biomedical Ethics and History of Medicine (IBME), University of Zurich (UZH); Department of Management, Technology and Economics (D-MTEC), ETH Zurich.

  • Prof. Dr. Tobias Kowatsch, School of Medicine HSG & Institute for Implementation Science in Health Care, University of Zurich & D-MTEC, ETH Zurich

You will work in a highly interdisciplinary environment at the intersection of management, medicine, technology, and public policy, with strong access to real-world data and decision-makers.

Candidate Profile

We are looking for a highly motivated candidate with the ambition to shape the future of prevention-driven healthcare.

Required Qualifications

• Master’s degree in computer science, machine learning, human computer interaction, behavioural and cognitive sciences, or a related field, with a minimum grade of 5.0 (Swiss grading system)
• Excellent proficiency in English and German (written and spoken)
• Willingness to work in a collaborative, interdisciplinary team
• Experience or strong interest in behaviour change interventions, stress management, or physical activity promotion
• Excellent organizational skills, attention to detail, ability to also work independently

Desired Background

• Experience working with text- or voice-based conversational agents, chatbots using large language models and agentic frameworks and protocols (e.g. model context protocol)
• Experience with the concept of just-in-time adaptive interventions (JITAIs)
• Interest in digital health for aging populations, prevention and health equity

What We Offer

• Fully funded 3.5-year PhD position, starting May or June 2026
• Competitive Swiss doctoral salary
• Unique access to Switzerland’s leading health insurers and public health institutions
• High academic freedom combined with strong real-world relevance
• Excellent publication and career opportunities in academia, policy, and industry
• A collaborative, international, and impact-driven research environment

Application

Please submit one single PDF document containing the following items in this exact order no later than 31st of January 2026 to rasita.vinay@uzh.ch:

1. Cover letter in English, addressed to

   Dr. Rasita Vinay, Prof. Dr. Tobias Kowatsch

   motivating why you want to (a) pursue a PhD at HSG and (b) work on prevention, agentic health interventions, and this specific topic

2. Curriculum Vitae (CV) in English

3. Transcript of records / overview of grades

4. At least two letters of recommendation

5. Written reflections on healthy longevity for middle-aged and older adults (see also the related work below)

   a) One qualitative reflection on one selected intervention that you would develop, focusing on either stress management, physical activity or health education (minimum 1 page), and

   b) One quantitative reflection, either on how you would evaluate this intervention in a real-world setting, or how you would enhance its personalization through predictive modelling and its evaluation (minimum 1 page)

Join Us

This PhD position offers a rare opportunity to combine rigorous academic research with real-world impact at national scale. You will help redefine how prevention, healthy longevity, and digital therapeutics can work economically, socially, and ethically in modern healthcare systems.

We look forward to your application.

Related Work

Applicants are encouraged to consult the following related work for inspiration when preparing their application:

1. Castro, O., Mair, J., Zheng, S., Tan, S., Jabir, A., Yan, X., Chakraborty, B., Tai, E.S., Van Dam, R., von Wangenheim, F., Fleisch, E., Griva, K., Kowatsch, T., Müller-Riemenschneider, F. (2025) The LvL UP Trial: Protocol for a Sequential, Multiple Assignment, Randomized Controlled Trial to Assess the Effectiveness of a Blended Mobile Lifestyle Intervention, Contemporary Clinical Trials, 1016/j.cct.2025.107833
2. Ollier J, et al (2021) Elena Care for COVID-19, a Pandemic Lifestyle Care Intervention: Intervention Design and Study Protocol, Frontiers in Public Health, 9(1543), 3389/fpubh.2021.625640
3. Barth, J., Schläpfer, S., Schneider, F., Santhanam, P., Kowatsch, T., Heinz, P., Held, U., Eicher, M., Witt, C. (2025) Mobile health intervention CanRelax reduces distress in people with cancer in a randomized controlled trial, npj Digital Medicine 8, 269, 1038/s41746-025-01688-x
4. Brill, E., Vinay, R., Nißen, M., Joshi, P., Klöppel, S., Kowatsch, T. (2025) Toward a Smartphone-Based and Conversational Agent–Delivered Just-in-Time Adaptive Holistic Lifestyle Intervention for Older Adults Affected by Cognitive Decline: Two-Week Proof-of-Concept Study, JMIR Formative Research, 9:e66885, 2196/66885
5.  Vinay, R., Uetova, E., Tommila, N.C., Biller-Andorno, N., Kowatsch, T. (2025) GRACE, A Hybrid Rule- and LLM-based Embodied Voice Assistant for Cognitive Stimulation in Older Adults: A Pilot Study Assessing Technical Feasibility, Technology Acceptance, and Working Alliance, JMIR Aging, 10.2196/76489
6. Heinz et al (2025). Randomized Trial of a Generative AI Chatbot for Mental Health Treatment. NEJM AI, 2(4), AIoa2400802. 10.1056/AIoa2400802
7. Flathers et al (in print) Contextualizing Clinical Benchmarks: A Tripartite Approach to Evaluating LLM-Based Tools in Mental Health Settings, https://www.digitalpsych.org/uploads/1/2/9/7/129769697/accepted_version.pdf