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to candidates from a broad range of AI subfields, including, but not limited to machine learning, generative AI, computer vision, representation and reasoning, natural language processing
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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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required. • Programming skills are required. • Knowledge of Natural Language Processing and Machine Learning is preferred. • Fluent English required, both oral and written. French is appreciated but
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extraction. So, from one side, there is a need for parsimonious machine learning approaches to classify, reconstruct and possibly segment 3D shapes. From another point of view, the aim of this PhD is to
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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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. Its human size fosters close interaction between students and lecturers, creating a personal atmosphere. With 1,600 members of academic staff for 6,700+ students and 1,000+ PhD candidates, students feel
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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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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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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