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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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the country of host institution for more than 12 months in the 36 months immediately before their date of recruitment. Must fulfil the conditions for enrolment to PhD programme at the host institution
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optimality and allows influence to be assessed at various stages of optimization. 2. Addressing Computational Challenges** Applying influence functions to deep neural networks presents **computational
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the conditions for enrolment to PhD programme at the host institution. For details about the criteria, please refer to detailed description of positions found at https://desire-msca.eu/ or contact desire-project
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Decadent Food and Drink from the 19th to the 21st century We are recruiting a PhD candidate for a fully funded project within DECADOCS, a pioneering research and training program enabled by the Marie
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The person recruited will carry out a doctoral thesis under the
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» Control engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 12 Oct 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status Full
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Marie Sklodowska-Curie Action (MSCA) Doctoral Networks
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through the EU Research Framework Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD will be carried out within the framework of a
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 23 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