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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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position is part of a large-scale research project around immune response in neurodegenerative disease. The PhD student will be part of a data science team working on data analysis and experimental design as
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat
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landscape, from SMEs and start-ups to OEMs and large-scale global manufacturers. For more information please visit the MTC website . Contact For further information on this PhD position please contact Dr
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Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace, mechanical
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multimodal machine learning, large language models, and fairness and uncertainty evaluations. The PhD student will benefit from: State-of-the-art AI computing recourses for large-scale model training including
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and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment. Learn More: The Dynamics Research Group
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of the top tier dynamics groups in the World with 15 academic staff and over 50 PhD students. The Group covers a wide range of fundamental and applied research and has a large group of collaborators. You will