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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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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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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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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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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