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postdoctoral Research Fellow (RF) position for one year with a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML
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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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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly
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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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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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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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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
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ensure UK-leading compute capability. These investments build on a major recent expansion of our academic staff and investment in our teaching and learning provision, with our Collaborative Teaching