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transfer learning to transpose the recurrent neural network (RNN) model available for supercritical CO2 power cycles to other cycles. Since thermodynamic conditions vary greatly depending on the fluid and
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, adsorption phenomenon, surface reconstruction and more. On top of these properties determined at zero temperature, we build thermodynamic models to provide macroscopic properties depending on external
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should demonstrates: - Solid knowledge of thermodynamics, heat and mass transfer, and energy conversion systems; - Strong interest in applied research combining modeling and experimentation; - Expertise in
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cellular signaling remains a key challenge in biology and is at the heart of the project. The PhD student will be in charge of the molecular modelling aspects of the multi-scale approaches undertaken by
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chemistry, materials science or equivalent having carried out research in an experimental field related to the thesis. Core competencies: crystal growth thermodynamic modeling chemical and physical
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environment featuring a wide range of research activities, including QM/MM simulations, ionic liquid simulations, and excited-state characterization. The aim of this PhD thesis is the atomistic modeling