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Programme for Scientific Discovery (https://acceleratescience.github.io/index.html ) is a high-profile University initiative promoting the use of machine learning (ML) to tackle major scientific challenges
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The Blended Learning Service works collaboratively across the University with academics and professional staff in support of teaching, learning, and assessment to further institutional developments
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We wish to appoint a physicist or data scientist with relevant experience and skills in MRI and/or machine learning to work with the Department of Radiology. The department has access to state
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and work well within a team. This is an excellent opportunity for someone who is eager to learn and develop their skills in the audio visual industry. Training and personal development will be supported
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platforms will be designed, capable of efficiently exploring complex mixed variable design spaces on the microlitre scale. Our multisite reactor network will be driven by next generation machine learning
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(UKHSA), and aims to investigate use of machine learning for prediction of infectious diseases outbreaks. Using data from routine surveillance of respiratory infections in the first instance, the
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basic statistical knowledge. Previous experience in cancer data analysis, machine learning, and exposure to diverse imaging modalities will be advantageous. The role offers training from senior
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advice, guidance and support to current and prospective disabled and neurodiverse non-matriculated students (including students with Specific Learning Difficulties, ADHD and Autism Spectrum Conditions
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Algo Trading, Bitcoin, AI and Machine Learning to offer relevant teaching for skills in those emerging areas. The class size is now more than double its original size, with 90+ students made up of a wide
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persons whose work falls within the broad fields of applied mathematical analysis and the mathematics of computation and data science, which includes PDEs, numerical analysis, the mathematics of machine