MAC is looking to hire a post-doc for the data rationalization of metabolomic and multi-omics results obtained for studies of hypertension. You will be part of the EU funded Hypermarker consortium (www.hypermarker.eu
) for the personalized pharmacometabolomic optimisation of treatment for hypertension. In this project metabolomics data will be collected to find biomarkers and improve metabolic predictive models for blood pressure control, develop a pharmacometabolomic-based predictive model for treatment of hypertension, and validate this predictive model with a prospective clinical trial. You will search for combined metabolomic markers to predict the efficacy of the drug response for hypertension. You will attempt to identify potential causal links between these metabolomic biomarkers, physiological parameters and real-world observations and reported data in clinical papers, represented as directed graphs or causal models. This knowledge graph approach allows to rationalize findings and to better interpret the unmet biochemical need of non-responders to antihypertensive medication and can thus help to identify possible future treatment targets. For the knowledge graph you will use Euretos’ AI platform. You will also be part of the Leiden Institute for FAIR and Equitable Sciences, in which expertise and an ecosystem for the equitable and privacy preserving reuse of health care and life science data and services will be developed, together with academic (e.g. TNO, LUMC, GoFAIR Foundation) and industrial partners. Key responsibilities
- Focus on 1) integrating existing knowledge related to blood pressure in a knowledge graph, 2) rationalizing metabolic markers related to blood pressure and blood pressure control, and 3) supporting the development of a model to predict hypertensive treatment outcome. You will work on this together with researchers in the Hypermarker research team.
- Co-supervision of PhD students.