Postdoctoral Position - Complexity Science Approach to Adaptive Pandemic Management (COVID-Complexity Study)

During the COVID-19 crisis, pandemic management proved highly complex because of strongly interacting centralized and decentralized decision making in combination with having multiple incommensurate goals (physical health, mental health, economy, etc.). This unavoidably resulted in uncertainty about health care effects and lowering societal resilience. In this NWO funded project we will apply complexity science methods to create an improved, more resilient management strategy, in four directly linked work packages (WP):

• WP I. data-driven multi-domain resilience operationalisation;
• WP II. group model building;
• WP III. computational modelling;
• WP IV. ‘flight simulator’ based scenario workshops on adaptive decision making.

Having policy makers participate in group model building and pandemic simulations should result in proof-of-principle testing of adaptive pandemic management to improve handling uncertain and multi-domain effects. For each of the four work packages we offer a two year post-doc position; this position pertains to WP3. The postdocs will closely collaborate in applying complexity science tools to improve management for policy and decision makers who also will participate as stakeholders. The project is supervised by a highly interdisciplinary study group with scientists from complexity science, medical microbiology, management, and computational science.

Are you as intrigued as we are by this possibility to study and improve societal resilience and overall management in future pandemics and in the interdisciplinary application of modern methods of epidemiology, management science, computational modeling, and in bridging the gap between science and (de)central policy making? Do you want to create impact by applying your research skills in an urgently needed complexity science project, as in the future pandemic-like crises are likely to re-occur? Are you interested in research on the interacting societal domains of health care, finances and education, characterized by their complexity, uncertainty and interactions between decentralized and centralized decision making? Do you consider it a challenge to work in a multidisciplinary, interfaculty environment? As the future of medicine and society is determined by complex, interdisciplinary problems, we offer you the challenge to work at the frontier of relevant research with complexity science methods fitting these challenges.

What are you going to do

The ideal candidate is ambitious and passionate about working towards interdisciplinary scientific and societal goals. He/she is eager to use these methods in developing new insights on adaptive pandemic management from a complex systems perspective.

The tasks of the candidate in the project are summarized as follows:

• translating qualitative domain knowledge (Causal Loop Diagrams, co-created in WP2; CLD) into quantitative system dynamics models (SDM) that can simulate hypothetical scenarios;
• using epidemiological data to inform/calibrate computational modeling;
• quantifying the various types of uncertainties in the model;
• quantify different policy targets in a single model (e.g., health, economic, ethical);
• quantify the resilience of the modeled system;
• developing interactive simulation workshops for stakeholders;
• join the effort to design a new adaptive management framework that makes use of co-created models and resilience concepts.