Master's internship Position - AI-driven exploration of diabetes burden through patients' perspectives

Project Overview

Living with type 1 diabetes (T1D) requires complex daily management and constant monitoring that is burdensome for people with T1D and contributes to adverse psychological outcomes. These include diabetes distress (DD), which refers to the negative emotional or affective experience resulting from the challenges and demands of living with diabetes. Standard questionnaires, like the Problem Areas in Diabetes (PAID) scale, capture some aspects, but may not cover hidden drivers of DD which people with T1D express in their own words.

Objectives

The goal of this internship is to generate new insights into diabetes burden and well-being by analyzing free-text responses from the SFDT1 study. The project aims to apply machine learning (ML) and natural learning processing (NLP) to identify recurring worries (e.g. “fear of complications”, “loneliness”), integrate psychological and behavioral frameworks to deepen the interpretation of patient concerns, and link these findings with clinical and psychosocial measures to highlight drivers of diabetes distress that may inform future interventions.

Key skills :

We welcome applications from students with diverse backgrounds & skills :

• Data science / machine learning / NLP : experience with Python or text analysis tools
• Psychology / public health / behavioral sciences : interest in diabetes, patient-reported outcomes, qualitative research
• Biomedical / health sciences : familiarity with diabetes, digital health, and data science

Duration : 6 months (full-time)

Location : Luxembourg Institute of Health, Department of Precision Health, Deep Digital Phenotyping Unit

Start date : Anytime between February and April 2026

Supervision : Dr. Guy Fagherazzi, Ms. Dulce Canha

Applications including a cover letter and a curriculum vitae should be sent before the 30th November 2025 via our website www.LIH.lu / jobs.

Gender Equality

The LIH is an equal opportunities employer. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff. The LIH is attentive to gender representation among its leadership staff and aims to eliminate obstacles to the recruitment and promotion of female leaders and their career development.

APCT1_LU