Metabolic diseases are a burden on the European population and health care system. It is increasingly recognised that individual differences with respect to history, lifestyle, and genetic make-up affect disease progression and treatment response. A Systems Medicine approach, based on computational models fed with individual patient data, has the potential to provide the basis for a personalised diagnosis and treatment strategy. The PoLiMeR consortium (Polymers in the Liver: Metabolism and Regulation) has identified the inherited, liver-related diseases of glycogen and lipid metabolism as the ideal starting point for innovative research training in personalised ‘Systems Medicine’. These diseases are life-threatening for children. Since each specific disease is rare, research efforts are diluted. Our system-based perspective opens possibilities for the application of novel drugs and diagnostic tools to a range of different diseases. As a PhD student in this project you will be part of a highly international team of young researchers. You will have your individual research project at your host organisation, focusing on your discipline of interest.
The PhD student will provide FAIR data management services to the PoLiMeR consortium based on the FAIRDOM Hub (the data sharing site hosted at HITS) and on GitLab. The PhD student will use the database generated in the PoLiMeR project together with pre-existing and public data in the to determine how changes in data collection, metadata quality and data feature extraction affect the information that can be found. The PhD student will seek to improve existing tools for data collection as well as querying at critical points.
We are looking for graduates in computer science who are aiming for a Doctorate. This topic is particularly interesting in that it provides quite general challenges (How to I tailor information retrieval and information storage algorithms such that they work best in combination?); at the same time, there are *real* users to cater to. These users can help in finding out the best combination of data enrichment/search algorithm in real life, in an exciting application area: The life sciences.