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

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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.
What do we require

Qualities of the ideal candidate:

  • computational modeling (differential/difference equations; stochastic dynamics; system dynamics);
  • uncertainty quantification (statistical, systematic, model uncertainty; forward and backward propagation; parameter sensitivity analysis);
  • statistical data analysis (p-value; (non-) parametric hypothesis testing; time-series analysis; (non-linear) correlation);
  • model calibration (Monte Carlo/MCMC techniques; likelihood function; local/global parameter optimization techniques; Pareto front);
  • complexity science and related concepts (resilience/robustness; tipping points; bifurcations/critical points);
  • an MSc and/or PhD degree in Computer Science, Computational Science, Complex Systems, (Applied) Mathematics, Statistics, Statistical Physics, or a closely related field;
  • a strong scientific interest in co-developing computational models in collaboration with scientists and stakeholders from various disciplines;
  • experience with mathematical/computational modelling/dynamical systems;
  • proficiency with probability theory and statistics;
  • fluent proficiency in English, both written and spoken.
Our offer

A temporary contract for 32 hours a week, preferably starting on 3 January 2022 for two years. The salary, depending on relevant experience before the beginning of the employment contract, will be €2,836 to €4,474 (scale 10) gross per month, based on a fulltime contract (38 hours a week). This is exclusive 8% holiday allowance and 8.3% end-of-year bonus. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities is applicable.

Are you curious about our extensive package of secondary employment benefits like our excellent opportunities for study and development? Take a look here.

Uren:
32 hours per week
Dienstverband:
parttime

Vaardigheden

Opleiding

PhD

Wat wij bieden

Contract:
Parttime
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