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Keywords: theoretical biophysics, machine learning, kinematics, (structural) biology. Context. Machine learning techniques have made significant progress in prediction of favourable structures from
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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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are particularly interested in candidates who combine computational biology, data science, and machine learning/AI with deep biological insight. While wet lab activities are welcome, they are not mandatory. However
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1- Context and Objective: Monitoring tumor response using clinical imaging, such as CT or FDG-PET, has become a
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centre at Universit´e Cˆote d’Azur, I3S Lab (Universit´e Cˆote d’Azur and CNRS) in collaboration with the Machine Learning Genoa Centre (MaLGa) at the University of Genova (Italy). The candidate will be
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welcome applications from candidates with a strong background in optimization, AI, or computer engineering, and who are excited by interdisciplinary challenges. Skills and interests we are looking
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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
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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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computation of visibility for the whole domain is intractable due to its high computational complexity, we will explore leveraging machine learning techniques such as reinforcement learning for the efficient