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requires good theoretical and hands-on expertise. Perfectly suited for those at the intersection of theory and application. Develop customized ILC and parameter optimization algorithms for industrial
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background in computational modeling of behavioral data Experience with model fitting, parameter estimation, and model comparison Proficiency in at least one scientific programming language (e.g
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Bayesian statistics and neural and Gaussian-process emulators for accelerating parameter estimation and uncertainty propagation Selective cross-scale evaluation using complementary ecosystem observations
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, oscillation, response-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB , R, or related computational environments
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observable reconstruction. Experience with uncertainty quantification, covariance modeling, sensitivity and robustness analysis, Bayesian inference, inverse problems, parameter estimation, or model
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modeling, especially microbial-explicit soil carbon models. Model-data integration, data assimilation, parameter estimation, uncertainty analysis, or Bayesian/statistical modeling. Grassland ecology, soil
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optimization (e.g., extremum seeking) for dynamic systems with unknown but measurable performance functions. Experience in system identification and online time-varying parameter estimation algorithms
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, quantitative biology, or a related field. Experience in ABM development and ODE/PDE modeling and analysis. Ability to select, implement, diagnose, and adapt parameter-estimation or statistical-inference
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governed by superinfection and co-transmission on the mosquito-to-human level. These overlapping, non-linear processes obfuscate estimation of malaria epidemiological parameters from parasite genetic
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work with computationally intensive analysis routines Evidence of skills in image or bio-signal (e.g. ECG) analysis Evidence of skills in model personalisation: parameter estimation, uncertainty