97 parallel-and-distributed-computing-"Meta"-"Meta" positions at Nature Careers in France
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, interdisciplinary group engaged in civil, electrical and mechanical engineering, driving forward innovative research and solutions. It also has an internationally leading profile in computational science and
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The successful candidate will develop computational approaches to discover, model, and develop therapeutic strategies. Examples of potential approaches include: -Network Modeling: Creating
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well as computational modeling. The development and numerical implementation of novel methods has become a key issue in modern oncology, both in terms of understanding the biology of cancers and for medical oncology
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close as possible to tumor sites to deliver its high-energy radioactive radiation, allowing the destruction of targeted the cells. Research program: Belonging to the halogen series, astatine exhibits in
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learning, focusing on identifying abrupt shifts in the properties of data over time. These shifts, commonly referred to as change-points, indicate transitions in the underlying distribution or dynamics of a
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statistics and machine learning, focused on identifying abrupt shifts in the properties of data over time. These shifts, known as change-points, indicate transitions in the underlying distribution or dynamics
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data-silos, like hospitals that cannot share their patients' data [4]. Research goal: One of the main scientific challenges of FL, in comparison to other forms of distributed learning, is statistical
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the ability of neural networks to learn unknown posterior distributions distributions. Their use in the field of image microscopy, however, remains limited. The purpose of this PhD thesis is to develop
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fields for several applications in the field of computer vision and inverse problem [SLX+21]. As far as the modeling of data term between distributions is concerned, one idea would be also to follow
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The candidate should preferably have a PhD in Computer Science or Robotics with a solid background on deep learning and 3D scene understanding. Experience with LiDAR and Computer Vision is a plus. The candidate