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.

Selection criteria

• Recent PhD in the field of biochemistry, physiology, systems biology or computational disease modelling, data sciences, or similar.
• Master’s degree in Chemistry, Biochemistry, Biopharmaceutical Sciences, Life Sciences, Biomedical Sciences, Data Sciences, or similar, preferably with a focus on Biochemistry
• Strong affinity with data science and biochemistry
• Independent and creative team player with strong communication skills
• A high degree of own initiative and autonomy
• Proficient in spoken and written English

Terms and conditions
We offer a full-time 1-year term position for initially one year. After a positive evaluation of the progress the appointment will be extended by two years. Salary ranges from € 3.226 to € 5.090 gross per month (pay scale 10, depending on experience) in accordance with the Collective Labour Agreement for Dutch Universities.

Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3 %), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break.

D&I statement
Diversity and inclusion are core values of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop their full potential. Diversity in experiences and perspectives enriches our teaching and strengthens our research. High quality teaching and research is inclusive.

Universiteit Leiden

Research at our faculty
The Faculty of Science at Leiden University is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are committed to expand fundamental knowledge by curiosity and to look beyond the borders of their own discipline; their aim is to benefit science, and to contribute to addressing the major societal challenges of the future.

The ambition of MAC at the LACDR is to develop innovative analytical strategies for metabolomics-driven health monitoring and systems biology studies. Understanding the intricate balance between health, disease and adaptation to challenges relies on the identification of systematic changes during time. We believe that health monitoring and personalized medicine approaches can be achieved with clinical studies involving multiple patient samples, often in combination with stem cell derived in-vitro or organ-on-a-chip models. MAC has been a pioneer in metabolomics since early 2000, and combines advanced metabolomics technologies, innovative sample preparation and robotics for clinical and in-vitro samples, and computational and FAIR digital twinning expertises. MAC houses a beyond-state-of-the-art metabolomics and mass spectrometry lab with automated and robotic sample preparation, access to state-of-the-art cell culture and advanced imaging facilities. MAC is involved in many national and international exciting academic, clinical and industrial collaborations, aiming to link metabolomics and multi-omics data with real world health data, and has the ambition to translate findings and technologies into healthcare and drug research.