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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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this, the Fellow will implement a universal design methodology for such fluids of complex rheology, using a Machine Learning (ML) algorithm to be incorporated in a Computational Fluid Dynamics framework. Training
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection, tissue characterization, and visual guidance of robotic intervention. This is a full time
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. You have experience in matrix algorithms, data compression, parallel computing, optimization of advanced applications on parallel and distributed systems. An excellent scientific track record proven
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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algorithms. The research focuses on wind energy applications, creating a compelling sustainability narrative: developing more efficient computational methods to optimize wind farm performance, which in turn
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goals As a researcher in this project, you will work on mathematical models for describing the radio environment and to design algorithms for estimating, for example, the location and spectral