41 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in France
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computational framework, integrated with deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit
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Description of the offer : We are opening two positions in the field of “Metallic and Oxide Materials for Orbitronics and Magnonics” at Institut Néel, CNRS, Grenoble The first position (PhD
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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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spoken and written is required The candidat must have a PhD in computer science, machine learning, or computational biology The position is available immediately and will remain open until filled
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found on hpc.uni.lu . The activities include classical HPC applications such as simulation and modeling, but also artificial intelligence and machine learning, bridging computational science, with data
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activities Present results in international conferences and workshops Your profile A PhD degree in Computer Science, Physics or a related field Strong background in understanding plasma physics and
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in Artificial Intelligence (Machine Learning and Statistics) at CentraleSupélec, · Joël Eymery, Head of the Nanostructures and Synchrotron Radiation Team at CEA Grenoble, · Jean-Sébastien
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SLICES-FR research infrastructure- Communicate results through presentations, conference papers, and project reportsProfile and skillsThe R&D Engineer must already hold a PhD degree in Computer
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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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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