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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
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with a high pulse power device. Another study intends to develop non-thermal electrons distribution which is of trendemous importance for UHI experiments. Durée du contrat (en mois) 2 ans + 2 x 1an
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in the field of operational research and/or machine learning algorithms would be a plus. In accordance with the commitments made by the CEA to promote the integration of disabled people, this job is
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