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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 the correctness of software and hardware systems using machine learning. Recent advances in neural certificates—such as neural model checking and neural termination analysis—have shown promising results in
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analysis and inversion of gravity gradient data from quantum technology sensors Develop data interfaces to enable integration with multi-modal sensing platforms Apply advanced data inference techniques
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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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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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40 hubs promoting sustainable, car-free travel. The Research Fellow will help refine hub design, assess impacts using HLTH frameworks, and model co-benefits to support future investment and stakeholder
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techniques including molecular characterisation techniques such as NMR spectroscopy, GPC etc. as well as thermomechanical analysis such as DSC, DMTA etc. and particle characterisation including light
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of the responsibilities outlined below Design and conduct bioinformatic data analysis pipelines under the guidance of the research supervisor to test the project hypotheses and complete project aims Develop research