With the introduction of the concepts of frailty and multimorbidity, medicine has made progress in the prediction and quantification of health risks of (older) individuals, i.e. who will be affected by disease and impairment or not. However, medicine is still largely unable to quantify how people respond when their health is challenged by the occurrence of intermittent disease or by treatment such as surgery or chemotherapy. Also, medicine is unable to predict if and how they will recover from such perturbations. This knowledge is crucial for personalized medicine for older individuals as well as for transforming healthcare to be prepared for the quickly growing health care demands of the aged population.

Resilience is a concept that originates from the study of ecosystems and is the dynamic ability of a system to withstand and recover from stress and perturbation. With the increasing availability of (sensor based) time series on health indicators and the advent of new (dynamic) indicators of resilience, we believe this concept may offer a means to begin to quantify resistance and recovery in medicine, but it needs to be developed and validated for use in this area. This will be your main task.

We offer you the challenge to work at this frontier of clinical research in a multidisciplinary environment where you will collaborate with experts from medicine, epidemiology, systems dynamics, computational science and ecology. As such, the project is closely connected to the work on tipping points and resilience of Prof Marten Scheffer and Dr Ingrid van de Leemput, affiliated to the Wageningen University, and connected to the SPARCS (Synergy Programme for Analysing Resilience and Critical TransitionS) research network.

As a post-doctoral researcher, you will have a leading role in an ambitious and inspiring new research line on resilience in geriatric medicine of the Department of Geriatric Medicine. Projects of this Resilience line focus on studying and modeling resilience of older patients during hospital admission and of older persons with cognitive impairment. Alongside Evidence Based Medicine principles as a first basis of our work, the department complementarily has chosen the complex systems science paradigm to integrate and develop our patient care, training and research activities, as we believe this offers the best chances to arrive at personalized medicine and have true impact. The resilience research line fits seamlessly into this direction.

• You are evidently ambitious and passionate to improve the health and wellbeing of older persons. You are also be eager to translate these new methods to other researchers and students;
• You have PhD degree in biomedical sciences e.g. physiology, epidemiology or movement sciences;
• You are competent in advanced analysis techniques to model biological sub-systems (e.g. different organs) and have experience with the analysis of multivariate time series (e.g. wearable sensor based);
• You have advanced expertise in mathematical modelling (e.g. Matlab) as well as explanatory and prognostic epidemiological modelling (e.g. SPSS, SAS, Stata, R) and growth modelling;
• The candidate should be capable to:
  - communicate and collaborate interdisciplinary to bridge the different scientific fields;
  - flourish in a team of international researchers, both culturally and intellectually.

Tasks and responsibilities
The researcher will:

• Translate empirical data on time series of health indicators (cognition, motor function and cardiovascular physiology and biopsychosocial wellbeing scores) as well as data from perturbation tests in older patients during hospital admissions and older persons with dementia to an indicator of systemic resilience following the conceptual approach of Dynamic Indicators of Resilience (DIOR);
• Develop the concept of resilience in medicine and validate methods to predict the resilience of (older) individuals;
• Help to renew the field of geriatric research, which currently is dominated by static-measurements, with time series analyses linked to resilience modelling;
• Apply knowledge and methods from complexity science in studies on tipping points in human disease trajectories, specifically in older adults;
• Develop further research ideas and (co-)write grant applications for further application of complexity science, and network medicine methods in geriatrics.

Fixed-term contract: 2 year with a possible extension.

Scale 10: max € 58838 gross per year at full employment (incl. vacation bonus and end of year payments)

Radboudumc (university medical center)

Radboudumc strives to be a leading developer of sustainable, innovative and affordable healthcare to improve the health and wellbeing of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare. To get a better picture of what this entails, check out our strategy film.

Our key strength is medical life-sciences and clinical practice, with an impressive infrastructure comprising state-of-the-art technology platforms and (translational) research facilities. The Radboudumc is therefore uniquely positioned in the emerging Euregio and Dutch healthcare infrastructure to play a leading role in the new healthcare paradigm of prediction, prevention and personalised medicine.

The Radboudumc focuses on scientific health challenges of today, with an eye on emerging diseases of the future.

Read more about what it means to work at Radboudumc and how you can do your part.

https://www.radboudumc.nl/EN/Pages/default.aspx

What we offer:

• Excellent research team with high level past performance in clinical research, epidemiology, and applied qualitative research. Tenure track possibilities depending on your stage of career;
• Inspiring environment within a network of national and international top research groups. Participation in the Radboudumc Institute for Health Sciences and the Donders Research Institute for Cognitive and Medical NeuroSciences;
• Patient care environment with ongoing clinical research, including co-promotorship in PhD trajectories. Big scientific challenge at the cutting edge of currently available research methods, in an area with enormous societal impact because of our quickly aging societies.