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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 2 months ago
, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning, statistical learning or game theory is appreciated. Research experience is a plus. LanguagesFRENCHLevelBasic
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-depth expertise in Computer Vision and Photogrammetry. - Mastery of state-of-the-art Neural Rendering (NeRF, NeuS, SDF). - Knowledge of Photometric Stereo methods. Operational Skills: - Advanced
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. The successful candidate will be employed at the Department of Computer Science of the University of Luxembourg and have access to high-performance computing resources suitable for large-scale machine-learning and
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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, France [map ] Subject Areas: Probability Statistical Physics Mathematics Statistics Machine Learning / Machine Learning Appl Deadline: 2025/12/21 04:59 AM UnitedKingdomTime (posted 2025/11/25 05:00 AM
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic