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between complex instances such as point clouds, images or graphs. However, as the modern data are increasingly high-dimensional, OT is also now facing an old problem in optimization and statistical learning
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/CT imaging Description of the topic As this is an interdisciplinary "AI and medicine" project, co-supervision by a doctor and a statistical/machine-learning researcher is planned (iBV / Inria) 1
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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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a challenging problem. Candidate profile PhD on optimization and/or image processing. Strong background in applied mathematics, image processing, learning methods and algorithms. Good coding skills
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Master/engineer degree in computer science, applied mathematics, data science with background in image processing, imaging inverse problems, deep learning and optimisation. Good coding skills for numerical
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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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opportunity for an outstanding scientist to establish an independent research program at the interface of biology and computer sciences, in one of the five major DYNABIO-affiliated institutes (C3M, iBV, IPMC
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. Required Skills and Candidate Profile The project is intended for a candidate with: ➢ Skills in medical image processing and deep learning adapted to clinical applications. ➢ A good knowledge of Python
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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
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Context. The initial great promise of 3D geometric modeling and processing was to achieve for shapes what had been done in digital signal processing for sound and images. Over the last twenty years