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on the broad topic of Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate
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We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only
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We invite applications for a fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is available to UK (Home) candidates only
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the form of a human-expert informed reward function. Second, we aim for the integration of low-energy machine learning algorithms, so that the resulting AI model can run on a variety of devices, including
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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quantum chemistry (QM), machine learning (ML), and high-throughput experimentation (HTE). The objective is to develop a data-driven framework that enhances the efficiency and effectiveness of catalyst
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an exciting opportunity to integrate machine learning and statistical methods, focusing on improving the efficiency and scalability of statistical algorithms. The project will develop innovative techniques
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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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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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. Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image