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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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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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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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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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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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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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machine learning. Supervision will be provided by Prof. Ali Mazaheri, as well as Prof. Fang Gao Smith, and Prof. Helen McGettrick. The successful candidate will have a strong background in psychology
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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the potential to impact on protected groups and take appropriate action. Desirable Skills: Experience with machine learning or natural language processing. Knowledge of econometric methods for policy evaluation
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quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential Experience in applying computational methods to research