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central objective is to achieve a deep, mechanistic understanding of the processes that govern electrochemical performance and cycling stability. Your research will focus on elucidating and engineering
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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. Empa is a research institution of the ETH Domain. The Laboratory Chemical Energy Carriers and Vehicle Systems Laboratory conducts, develops and optimizes processes for renewable fuels. The group
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related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy
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field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy