Our research group works at the interface between nutritional medicine, digital health and computational life sciences. Our main aim is to develop a Metabolic Digital Twin, a data-driven model of individual metabolism that enables personalised nutritional strategies.
The ideal candidate is keen to explore the future of personalised nutrition and is motivated to combine biomedical reasoning with modern AI methods.
Identical meals can trigger very different metabolic responses, both between individuals and within the same person over time. To capture this variability, we have developed a proprietary algorithm that estimates blood glucose levels in real time from smartwatch data, without the need for invasive CGM. Combined with our molecular food database, this enables us to link dietary intake to physiological responses at high resolution. Building on these technologies, we aim to develop predictive models that simulate individual responses to meals and support recommendations for the right meal, for the right person, at the right moment.
A Fellow who develops AI models to predict how a given meal affects an individual’s blood glucose levels over the following hours. The Fellow will work with existing and newly collected datasets and use time-series foundation models such as TimeGPT to build, compare and validate these prediction models.
The Fellow reviews the literature, prepares the datasets, defines input features, and builds, compares and validates different modelling approaches. The work is carried out independently within a clearly defined project, supported by regular supervision, research group meetings and discussions with experts from the fields of nutrition, medicine and data science. By the end of the project, the Fellow produces an overview of relevant time-series foundation models and a validation demonstrating which approach is best suited to the task.
