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, with the longer-term objective of improving the prediction of fast-charging behaviour. Your project will be to build or adapt a machine-learning interatomic potential for lithiated graphite using density
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their 4th or 5th year of studies (M1, M2 or gap year) - Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework
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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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have skills in eukaryotic cell biology, electron microscopy, and bioimage analysis. You have a basic knowledge in integrative structural biology, and in AI / deep learning approaches and/or sub-tomogram
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to strengthen your skills, acquire new ones, and enhance your mission. A work-life balance recognized by our team members. Remote work options to reduce commuting time and improve your quality of life. A rich
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your skills or acquire new ones, excellent medical insurance, subsidies for the use of public transport (85%) and the staff canteen, holidays and a variety of cultural and sports activities. Home working
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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