Student (f / m / d) for master thesis in data-driven health research

Uniklinik RWTH Aachen; Aachen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

A leading academic institution in Germany seeks a student for a master thesis focused on agentic AI in circadian metabolic research. The project involves analyzing data from wearable sensors and aims to develop innovative AI pipelines to support health research. Ideal candidates will have an interest in metabolic diseases and experience with data analysis and wearable technology.

Qualifikationen

• Background in data-driven health research and AI.
• Familiarity with wearable technology and data analysis.
• Interest in metabolic diseases and circadian rhythms.

Aufgaben

• Develop agentic AI pipelines for data analysis.
• Integrate multimodal datasets for research.
• Explore novel methodologies for circadian-metabolic research.

Jobbeschreibung

Student (f / m / d) for master thesis in data-driven health research

Topic : Agentic AI for Circadian Metabolic Research as a Multimodal Analysis of Nutrition, Glucose, and Light Exposure Data in Healthy Adults

Starting Date : October 1st 2025

Supervising Institutions :

• Institute of Occupational Medicine, Healthy Living Spaces lab, RWTH Aachen University (Dr. Jan-Frieder Harmsen)
• Department of Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen (Prof. Dr. Carolin Schneider)

Background :

Circadian misalignment is increasingly recognized as a key pathophysiological driver of metabolic diseases, especially in populations exposed to night shift work. Recent advances in wearable sensor technology, such as continuous glucose monitoring (CGM), light exposure tracking, and smartphone-based food photography, now allow for highly granular 24-hour data acquisition in real-world settings.

Conventional data analysis approaches are often insufficient for capturing the complexity of these multimodal, asynchronous datasets. Here, agentic artificial intelligence (AI) offers novel opportunities for both methodological development and scientific discovery.

Aims :

This thesis project aims to develop agentic AI pipelines for the integrated analysis of CGM time series, meal photographs, and light exposure data collected over 10 days in the everyday life of healthy young and older adults.

The dataset comprises ten consecutive days of data from 60 healthy participants, including 30 younger and 30 older adults. Each participant underwent continuous glucose monitoring (CGM), capturing interstitial glucose values at 15-minute intervals. Light exposure was continuously recorded using state-of-the-art wearable sensors, while food intake was logged via timestamped photographs taken with smartphones. All data have undergone initial quality control and harmonization.

The goal is to explore how modern agent-based AI frameworks can autonomously generate annotations, perform time-aware analyses, and support hypothesis generation in the context of circadian-metabolic research. The thesis is both methodologically innovative and biomedically relevant, with future directions to be defined.