PhD Candidate in Rehabilitation Medicine – Biological Age and Biomarkers
· AMSTERDAM, Netherlands ·
- Employment
- Contract
How can we reliably measure and influence biological age to support healthier ageing? Explore this question at the intersection of biomarkers, advanced data analysis and rehabilitation medicine in this PhD position within the national BIO COMPaSS project. Within the NWA research programme BIO COMPaSS (BIOlogical age-driven, COMmunal, Personalised System for Sustainable health promotion), universities, university medical centers, companies and citizens work together to motivate people towards a healthier lifestyle by providing insight into their biological age – a metric that often says more about health than chronological age. The project aims to develop an evidence-based set of biomarkers of ageing and to test how these can be used for sustainable health promotion. As a PhD candidate in Work Package 3 (WP3), within the Department of Rehabilitation Medicine at Amsterdam UMC, you will focus on the characterization of biological-age-related phenotypes. You will investigate which combinations of biomarkers (epigenetic, metabolic, organ-specific, functional and lifestyle-related) form patterns that provide information about someone’s current and future health and functioning. You will use rich existing datasets (for example large cohorts and registries) and work closely with other BIO COMPaSS PhD candidates who focus on causality, interventions and implementation. The insights you generate on phenotypes and biomarker profiles will be an important building block for personalized lifestyle interventions within BIO COMPaSS. Would you like to know more about the different phases within the PhD trajectory? You can read more about this on this page. As a PhD candidate, you coordinate and conduct the research within WP3 in close collaboration with clinical and methodological experts. Your work will be largely data-driven, always with a clear eye on application in rehabilitation practice. Your tasks include: Designing and conducting a systematic review of biomarkers of
biological age (epigenetic, metabolic, organ-specific, functional), including assessment of clinimetric properties. Selecting, linking, cleaning and harmonizing existing datasets, including population studies and clinical databases, in collaboration with partners within BIO COMPaSS. Applying advanced statistical and data-science methods (e.g. clustering, latent class/latent profile analyses, dimension reduction, multimodal integration, prediction models) to identify biological-age phenotypes. Characterizing the identified phenotypes in relation to health and functioning. Contributing to the translation of phenotypes into practical biomarker sets that can be used in clinical follow-up and lifestyle interventions. Publishing results in international peer-reviewed journals and presenting at (inter)national conferences. Working closely with PhD candidates, postdocs, clinicians, epidemiologists, data scientists and other researchers in the BIO COMPaSS consortium. From year 4 onwards, specific tasks will gradually be transferred to a second PhD candidate within WP3, with whom you will already collaborate closely from the start. You will complete your project with a PhD thesis. You have a strong analytical interest in ageing, lifestyle interventions, biomarkers and data analysis, and you are keen to conduct research with direct relevance for the health of people with (or at risk of) lifestyle-related conditions. In addition, you have: A completed Master’s degree in Biomedical Sciences or Epidemiologye. Affinity with ageing and biomarker research, preferably including epigenetic or metabolic biomarkers. Good programming skills in statistical software (e.g. R, Python or similar), or strong motivation and demonstrable basic skills to learn this quickly. Preferably experience with analyzing large and/or high-dimensional data (e.g. omics, extensive clinical datasets, registries). Interest in or experience with advanced statistical methods (e.g. multivariate models, causal
biological age (epigenetic, metabolic, organ-specific, functional), including assessment of clinimetric properties. Selecting, linking, cleaning and harmonizing existing datasets, including population studies and clinical databases, in collaboration with partners within BIO COMPaSS. Applying advanced statistical and data-science methods (e.g. clustering, latent class/latent profile analyses, dimension reduction, multimodal integration, prediction models) to identify biological-age phenotypes. Characterizing the identified phenotypes in relation to health and functioning. Contributing to the translation of phenotypes into practical biomarker sets that can be used in clinical follow-up and lifestyle interventions. Publishing results in international peer-reviewed journals and presenting at (inter)national conferences. Working closely with PhD candidates, postdocs, clinicians, epidemiologists, data scientists and other researchers in the BIO COMPaSS consortium. From year 4 onwards, specific tasks will gradually be transferred to a second PhD candidate within WP3, with whom you will already collaborate closely from the start. You will complete your project with a PhD thesis. You have a strong analytical interest in ageing, lifestyle interventions, biomarkers and data analysis, and you are keen to conduct research with direct relevance for the health of people with (or at risk of) lifestyle-related conditions. In addition, you have: A completed Master’s degree in Biomedical Sciences or Epidemiologye. Affinity with ageing and biomarker research, preferably including epigenetic or metabolic biomarkers. Good programming skills in statistical software (e.g. R, Python or similar), or strong motivation and demonstrable basic skills to learn this quickly. Preferably experience with analyzing large and/or high-dimensional data (e.g. omics, extensive clinical datasets, registries). Interest in or experience with advanced statistical methods (e.g. multivariate models, causal