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approaches using metal ion aggregation and intrinsically supramolecular chiral architectures will be explored. Marie Sklodowska-Curie Actions (MSCA) doctoral networks are joint research and training projects
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group investigates the cellular and molecular mechanisms wiring neuronal networks. The candidate will join a project aiming at understanding how migrating neurons, developing axons and endothelial cells
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biological imaging will also be evaluated. Marie Sklodowska-Curie Actions (MSCA) doctoral networks are joint research and training projects funded by the European Union. Funding is awarded to doctoral students
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exergetic analysis, including control strategy. The net monthly salary is €2,200 The contract duration is 3 years Where to apply E-mail Daniel.matt@umontpellier.fr Requirements Research
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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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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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— employees have access to a comprehensive training catalog covering both foundational and advanced topics, from data science and AI to coding best practices, cloud platforms, green IT, and soft skills. OCTO
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 21 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