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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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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
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comprise both the development of bioinformatics pipelines and the application of novel machine learning methods for interpreting microbiome and host ‘omics data from faecal, intestinal biopsy and saliva
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specific a focus on the applications of machine learning and artificial intelligence in higher education. Lead and oversee training and support provision to academic staff on the use of AI tools in
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Strong analytical skills and experience in developing and implementing machine learning/AI solutions using relevant languages and frameworks Excellent communication skills and proven ability to collaborate
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Language Processing (NLP) with a focus on large language models, deep learning, and multi-modal machine learning. The researcher will work on the project KAMAL Health: Knowledge-Augmented Multi-Modal Arabic LLMs
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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and kept up to date including maintaining records both paper and computer based as appropriate. Store, curate and back-up data to prevent data loss. Escalate problems that may affect the achievement