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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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limited to digital electronics (including FPGAs and VHDL) distributed network control, Data Integration, Management and Cybersecurity and integration of hybrid/electric vehicles into energy infrastructure
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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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and working groups as required; setting agendas, collating and distributing papers and minutes and ensuring follow up action is taken assessing and escalating as you deem appropriate issues and risks
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; Analysis; Bubble Dynamics; Combinatorics, Probability and Algorithms; Continuum Mechanics; Geometry and Mathematical Physics; Mathematical Biology and Healthcare; Mathematical Data Science; Nonlinear Systems
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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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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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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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OH advice to line management. Triage requests for clinical work by progressing requests and distributing clinical work fairly and expediently across the team to support meeting the Service standards