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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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, 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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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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control and grid-forming/grid-following operation; microgrids modelling; model order reduction; parameter estimation and model validation; large-scale or synthetic network modelling. Experience in