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Doctoral Network aiming to train 15 PhD researchers in the efficient and clean use of renewable synthetic fuels. Candidates will develop advanced skills in combustion science, chemical kinetics, and digital
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(Decarbonising Energy-intenSIve industries with REnewable synthetic fuels) is a Marie Skłodowska-Curie (MSCA) Doctoral Network, designed to train the next generation of specialists in e-fuel utilization. DESIRE
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The DESIRE project (Decarbonising Energy-intenSIve industries with REnewable synthetic fuels) is a Marie Skłodowska-Curie (MSCA) Doctoral Network, designed to train the next generation of specialists in e-fuel
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global anthropogenic CO2 emissions. Decarbonizing the cement industry to achieve net-zero is particularly challenging because a significant fraction of CO2 emissions stems from the intrinsic chemical
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networks, ensemble algorithms, and other advanced architectures, the objective will be to accurately predict the state of health (SoH) of batteries in the short, medium, and long term, including under
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 9 days ago
, 3], significant challenges related to running complex AI algorithms such as Deep Neural Network (DNN) inference on lightweight platforms with limited computational power and relying on potentially
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, workshops, and networking events. Application Procedure Applications should include: A cover letter describing motivation and fit for the project. A CV with details of education, research experience, and
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optimization of complex systems, intelligent data and information systems, as well as networks, distributed systems, and security. LIMOS stands out for its interdisciplinary approach, combining theoretical
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Networks in order to handle non-linear relationships between covariates and response variables. To this aim, the PhD student will join a consortium of researchers issued from different disciplines with a
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. This issue can have safety implications, particularly in closed-loop setups. Physically Informed Machine Learning (PIML), and in particular Physics-Informed Neural Networks (PINN), are less dependent on data