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Field
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energy, personalized medicine and climate science and we need your help to develop tools and workflows to drive exploitation of this world leading resource. You will also play a key role in fostering a
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web technologies Experience in teaching bioinformatics Previous experience with AI and/or machine learning approaches Interest in reproductive health and/or development of clinical tools and algorithms
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Martin and Professor Daniel Paulusma from Durham University and Professor Vadim Lozin from the University of Warwick. The general aim of the project is to develop algorithmic meta-classifications, which
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EPSRC-funded project, MAPFSI that will be focused on developing experimentally-validated computational algorithms for fluid-structure interaction problems including multiphysics effect of electromagnetism
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(EHR) data built by a multidisciplinary team of software developers, machine learning engineers, clinical researchers and health informaticians. The CogStack team is at the forefront of building
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to test the current algorithm and inform development of future algorithm refinements aimed at supporting diabetic foot ulcer (DFU) prevention through identification of temperature differences of > 2.2°C
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Job Purpose To make a leading contribution to the development of innovative muon tracking algorithms for cutting-edge applied nuclear physics research. The successful candidate will be expected
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm
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6. Desirable criteria Evidence of active collaboration with dry lab and co-development of algorithm for the prediction of epitopes. Downloading a copy of our Job Description Full details of the role
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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