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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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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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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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-speed cameras (in a newly renovated lab dedicated to our research group). A significant component of the analysis will include image processing, including data-driven methods and machine learning. You
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
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cobalt-free cathodes. The project can involve aspects of materials synthesis, x-ray diffraction and crystallography, scientific software development and machine-learning enhanced analysis depending
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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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in computer vision and intelligent transportation. Experience with tools such as MATLAB, Python or machine learning frameworks is highly desirable. Supervisor: Dr Ning Zhao (N.Zhao@bham.ac.uk
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc