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inversion techniques and signal processing. Strong programming skills, Proficiency in scientific computing (e.g. Python, MATLAB, or similar) for algorithm development and data handling. Experience with sensor
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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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uses, improving the AI and MRI algorithms, and linking them with information from biological studies on tumour tissue. This project harnesses AI to improve diagnosis and clinical decision-making leading
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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research active academic staff, around 20 postdoctoral researchers and around 50 postgraduates, with research groups in Algebra; Analysis; Bubble Dynamics; Combinatorics, Probability and Algorithms
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of existing image analysis software and generation of bespoke algorithms for advanced image analysis. This will typically require coding expertise in languages such as MATLAB, R and/or Python and familiarity
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to apply it in selected poor-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging
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Experience with machine learning algorithms and ideally experience developing novel methods Understanding of basic biological principles and experience interpreting ‘omics data Ability to analyse information
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signal processing Extensive research experience and scholarship on radar systems and signal processing, ideally for distributed radar sensors, and general knowledge of the effects of oscillator