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of data, services, interaction devices, and use cases influence the evolution of software and systems to guarantee the essential properties such as reliability, performance, autonomy, and adaptability
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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carbon species once deemed too unstable for practical use, were tamed through the pioneering work of Bertrand and Arduengo, who synthesized the first stable singlet carbenes. In particular, N
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take place from July 13 to 18. Other experimental and exploratory workshops may also be offered to doctoral students during their involvement in the project. - Based on their experience and practice in
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skills in experimental methodology, usage analysis, and statistical analysis. - Basic knowledge of AI - Ability to work in a multidisciplinary collaborative environment (leading meetings, planning
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through the manufacturing and characterization of test devices. To face the multiple challenges of the energy transition, whether at the generation, transport or use of electricity, extremely efficient
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using neural networks (DNNs). The PhD work will focus in particular on the prediction of extreme weather events and will use meteorological data bases provided by Météo-France [py4cast, 2025; Anemoi 2024
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its mechanical deformation and response to atmospheric conditions, which remain a major challenge for existing models. The use of neural emulators and differentiable programming techniques opens up new
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associated with pesticide use, together with the increasing incidence of resistance cases in pests and pathogens, have prompted global policy initiatives to reduce pesticide dependence. Integrating robotic
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for the early patterning of the developing brain - To use a variety of technologies to investigate CR cell specification, subtype diversity, and their impact on regionalization and lamination - To implement