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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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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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heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting
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for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and institutions, supported through dedicated
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training, exchanges, and career development for PhD students and postdocs. Learn more at https://www.muoniverse.ch/ . Muoniverse positions often serve as bridges between individual research groups and
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project aims to develop hydroborate-based all-solid-state batteries. A central objective is to achieve a deep, mechanistic understanding of the processes that govern electrochemical performance and cycling
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be considered. Where to apply Website https://academicpositions.com/ad/empa/2026/phd-position-in-operational-feasibil… Requirements Research FieldComputer scienceYears of Research Experience1 - 4
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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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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system