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Field
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transportation networks but also the case of wind farms, solar grids and IoTs. Consequently, developing and using machine learning tools to process these graph data is more important than ever. Such a tools need
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We are seeking for a highly-skilled and self-motivated candidate with a strong mathematical background to do a Ph.D. on the fundamental aspects of graph machine learning with applications
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for innovation since the early days of machine learning. In particular, building on recent developments on VAE and diffusion models, we focus on the role of physics in generative models. In generative
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structure bioinformatics and machine learning, who is also not afraid to participate in wet-lab experiments to generate the best possible training datasets. The human gut microbiome plays a crucial role in
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answering mathematical questions. You have a solid background in one or more of the following: functional analysis, numerical analysis, differential geometry, theoretical machine learning. You are motivated
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Group together with Bioinformatics Group at Wageningen University are looking for a PhD candidate with expertise in protein structure bioinformatics and machine learning, who is also not afraid
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The project you will work on lies on the boundary between AI and theoretical physics. Physics has been a source of inspiration for innovation since the early days of machine learning. In particular
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nonlinear interactions at the origin of such extreme events. In this project, we will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based
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will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based techniques to achieve the goals of (i) identifying precursors and mechanisms
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Vacancies PhD position on analysis of geometric machine learning methods Key takeaways We are looking for a motivated, theory-oriented PhD candidate to work on the project "A continuum view on