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Field
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This PhD position offers a unique opportunity to dive into the exciting area of reinforcement learning within the broader landscape of AI and machine learning. As a candidate, you'll have the chance
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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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parts. First, the candidate will develop machine learning models to assist gynecologists and embryologists in their decisions and advice regarding couples with fertility problems. We will focus
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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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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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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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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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Applications are invited for a PhD student to work on machine-learning guided and verifiably correct code generation. Creating optimised libraries is a difficult and time-consuming task, requiring