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-driven methods to characterize demand-side flexibility using e.g., by using a set of coupled stochastics differential equations or data-driven digital twins. As a result, we will aim at obtaining a
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on preferences the candidates will work along one (or more) of the following different directions: theoretical foundation involving quantitative models (e.g. stochastic, timed weighted, hybrid automata) and logics
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, Pytorch, etc Optimization techniques (e.g. gradient-based, stochastic, linear programming) Machine learning techniques Energy storage systems Furthermore, a successful candidate has: Excellent use
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during training, an effect attributed to the properties of the optimization technique. Intuitively, stochastic optimizers tend to converge to flatter minima in the complex loss landscape, which is believed