Scientific Machine Learning for Personalised Medicine and Precision Nutrition.
- Are you inspired by health care challenges?
- Are you eager to work on the development of cutting-edge modelling and AI approaches for personalised medicine?
- Are you our next PhD-candidate in scientific machine learning for precision nutrition?
Information In recent years, artificial intelligence (AI) and machine learning (ML) have shown great potential in healthcare, particularly in oncology, where models have been trained to predict personalised treatment strategies based on different omics measurements and improved long-term patient outcomes. However, these models often require large datasets, limiting their application across other biomedical fields.
This PhD project aims to push the boundaries of AI in healthcare by exploring scientific machine learning—an emerging branch of AI that integrates existing scientific knowledge into machine learning algorithms, reducing the need for large datasets. In this project you will develop a framework combining mechanistic mathematical models with machine learning techniques to create hybrid models capable of integrating high-dimensional omics data (such as genomic or microbiome profiles) with time-series data on metabolomics and clinical parameters.
The project will focus on applying these hybrid modelling framework to data from human dietary intervention studies to explore the role of gut microbiome composition and chronic low-grade inflammation in the development of cardiometabolic diseases. You will also investigate how these models can predict individual responses to various dietary interventions, potentially transforming precision nutrition and personalised disease prevention strategies.
We are seeking motivated PhD candidates with a strong interest in machine learning, mathematical modelling, and biomedical data analysis. This is an exciting opportunity to contribute to the development of next-generation AI tools in the biomedical field and make a significant impact on public health.
The position will be based at the Computational Biology groups in the Department of Biomedical Engineering under the supervision of dr. Shauna O’Donovan and Prof.dr.ir Natal van Riel. The Computational Biology Group consists of a multi-disciplinary team of researchers that work on developing novel computational approaches for digital twins and decision support systems in healthcare and the prediction of personalised treatments in the areas of cardiometabolic diseases and oncology. In addition to the research described above, you will supervise MSc/BSc students that will work on projects within the scope of your research.