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interconnected and closely intertwined scientific themes. The first targets the development of hybrid algorithms combining multi-physics modelling of electronic components, predictive control and machine learning
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MSCA Doctoral Network HINA - Hybrid INtegration of Alkaline niobate - tantalate films for advanced photonic and piezoelectric devices | Montpellier, Languedoc Roussillon | France | 2 days ago
be technology management including topics such as technology innovation and intellectual asset management. The opportunity for the DCs to explore and acquire these multidisciplinary skills within
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, as well as doctoral programmes and several Master’s programmes. Open to students in initial education as well as to professionals through lifelong learning, these programmes are delivered by our three
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of AI approaches at different levels of the model chain, i.e. by implementing end-to-end learning frameworks that link data, forecasts, and operational decisions, while incorporating diverse contextual
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with GREYC (UMR CNRS 6072), the computer science laboratory of Université de Caen Normandie, particularly in machine learning and graph-based approaches. Depending on the scientific questions addressed
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modelling (numerical simulations, e.g. matlab or python, or analytical modeling), OR optomechanics, OR high frequency devices. You are eager to learn and expand your knowledge! Basic knowledge of analog and
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of the multidisciplinary TrAI-SOC project, coordinated in collaboration with UGA's CERAG laboratory. SocSIM-K: https://lig-socsim-k.imag.fr/ LIG: https://www.liglab.fr/en Activities Design the game and define all of its
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of the multidisciplinary TrAI-SOC project, coordinated in collaboration with UGA's CERAG laboratory. SocSIM-K: https://lig-socsim-k.imag.fr/ LIG: https://www.liglab.fr/en Activities Design the game and define all of its
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
machine learning, explainable machine learning, fairness and data protection legislation. Privacy-preserving machine learning aims at learning (and publishing or applying) a model from data while the data
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with deep-learning, UniGEM aims to build a neural network to estimate epidemiological parameters of P. falciparum, the deadliest malaria parasite species (read [1] to learn more about the ideas behind