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natural processes and technological applications, from atmospheric phenomena like rain clouds to industrial processes such as spray cooling of electronics. Due to their ubiquitous presence, droplets have
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light, and optimize processes to produce them. The fellow will build the framework to model the processes at play and analyse their energy/entropy costs. She/he will work in close collaboration with
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) have shown promise in incorporating physical constraints into the learning process. Recent developments in geometric deep learning, graph NN and neural operators have further expanded the potential
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this paradigm to general history-dependent / rate-dependent behaviours still remains challenging and will be addressed in this project. The paradigm of model-free data-driven computational mechanics (DDCM
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the LPMMC (Grenoble, France), the company EDF and the startups Alice&Bob and Quandela. Role of the research fellow- The fellow will build the framework to model the processes at play and analyse their energy
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of tissue growth are not included in the design process. Research Hypothesis: Considering dynamics of ingrowth dynamics bone and tendon tissues should improve the live designing of dedicated scaffold
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: - reading and understanding theoretical physics articles, in particular with a view to deriving equivalent computer algorithms - develop and/or adapt a physical modem and/or a Monte Carlo type code in
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FieldPhysicsYears of Research ExperienceNone Research FieldTechnologyYears of Research ExperienceNone Additional Information Eligibility criteria - The candidate must have a PhD in the field of organic chemistry for
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of exapted TE genes. To be eligible, the candidate must earn a PhD with less than 8 year of research experience in European labs, and has not spent more than 12 months in France over the last three years
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how varying gravitational conditions affect manufacturing processes. Desirable skills: Knowledge of experimental design for microgravity conditions (e.g., parabolic flights, drop towers) is a plus but