PhD Student in Digital Psychology – voice-based monitoring in Diabetes
ABG-131890 | Thesis topic | |
2025-05-13 | Public/private mixed funding |
- Digital
- Health, human and veterinary medicine
Topic description
Background
People living with diabetes frequently face psychological burdens that remain underdiagnosed and undertreated. Mental health disorders such as diabetes distress, anxiety, and depression are prevalent in both type 1 and type 2 diabetes and are associated with poor glycemic control and increased risk of complications. The Deep Digital Phenotyping (DDP) Lab is pioneering a new generation of digital biomarkers – including voice biomarkers – to support the early detection and remote monitoring of psychological well-being in people with diabetes. This project builds upon the large international Colive Voice study and other ongoing translational initiatives to develop a voice-based digital health solution to alleviate the diabetes burden.
Project objective
The PhD candidate will work at the interface of psychology, digital health, and artificial intelligence to advance the development and validation of voice-based tools for mental health monitoring in diabetes. The project will leverage data from the Colive Voice dataset, the SFDT1 cohort study, and new clinical studies, with the objective of developing and piloting a voice-based diabetes companion app to alleviate diabetes burden.
Key Responsabilities
- Design and conduct analyses linking vocal biomarkers to validated psychological scales (e.g., PAID, PHQ-9, GAD-7).
- Apply natural language processing (NLP) and AI techniques to large-scale audio datasets.
- Collaborate with people with diabetes, diabetologists, software engineers, and clinical psychologists to develop and validate prototype tools.
- Contribute to qualitative and mixed-methods research to ensure user-centered design and clinical relevance of the solution.
- Lead scientific dissemination efforts (manuscripts, conference presentations, public engagement).
- Prepare scientific manuscripts for publication and present findings at national and international conferences.
- Support collaborations with academic and industry partners in the context of the Colive Voice and diabetes voice monitoring projects.
Training & Environment
This PhD position is hosted within the DDP Lab, under the leadership of Dr Guy Fagherazzi. The lab offers a dynamic and internationally recognized research environment at the forefront of digital health innovation. The candidate will engage in cutting-edge projects and benefit from mentoring in digital health innovation, vocal biomarker development, psychological research in chronic conditions, and research-to-practice translation. Dr Abir Elbeji will supervise the PhD student on the voice analysis, and Dr Aurélie Fischer on the co-design of the digital health solution.
Starting date
Funding category
Funding further details
Presentation of host institution and host laboratory
The Luxembourg Institute of Health (LIH) is a public biomedical research organisation focused on precision health and invested in becoming a leading reference in Europe for the translation of scientific excellence into meaningful benefits for patients. LIH places the patient at the heart of all its activities, driven by a collective obligation towards society to use knowledge and technology arising from research on patient-derived data to have a direct impact on people’s health. Its dedicated teams of multidisciplinary researchers strive for excellence, generating relevant knowledge linked to immune related diseases and cancer. The institute embraces collaborations, disruptive technology and process innovation as unique opportunities to improve the application of diagnostics and therapeutics with the long-term goal of preventing disease.
Institution awarding doctoral degree
Candidate's profile
Your profile
- MSc (or equivalent) in Psychology, Clinical Neurosciences, Cognitive Science, Digital Health, or related fields.
- Interest or prior experience in digital phenotyping, vocal biomarkers, and AI-assisted health tools.
- Basic proficiency in data analysis (Python or R); experience with speech analysis libraries or NLP is an asset.
- Strong scientific writing skills and a collaborative spirit.
- High motivation to work in an interdisciplinary and translational research environment.
- Fluency in English. Knowledge of French or another EU language is an advantage.
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