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or machine learning methods to tackle predictive questions. Proficiency in building and validating statistical methods and/or machine learning techniques in R or Python are also essential. Applicants
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with career stage). Essential Interview / Application / Test In-depth knowledge of Computational Intelligence/Machine Learning systems and methods, in particular those relevant to Explainable AI, Physics
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development, excellent time management skills and who is able to work on their own initiative, working methodically and accurately to follow procedures and instructions. Main duties and responsibilities First
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recruitment strategies for a novel prostate cancer screening study. Prostate cancer is the second commonest cause of cancer death in men in the UK. Currently there is no NHS screening programme, with men
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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this emerging research programme Applicants should be familiar with methods for estimating comparative effectiveness using RWD, e.g., NICE’s TSD 17 , NICE’s RWE framework . You will be encouraged to develop your
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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you to apply. Please ensure that you reference the application criteria in the application statement when you apply. Essential criteria Essential criteria Method of assessment 1 MSc containing
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statistics, health economics, or in a scientific discipline relevant to healthcare (assessed at: application) Have demonstrated experience in relevant research methods, for example through publications in peer