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experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools; Code synthesis
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cytometry will be an advantage. The project has a major computational component both for AI-driven modelling and predictions, and for bioinformatics analyses of wet-lab data. This will be performed by
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better position. A model is needed into which to feed critical information and retrieve cause/effect insights on which to base logical decisions. Biological information cuts across diagnostic boundaries
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models to assess the environmental impacts of bio-based products and systems using software tools such as OpenLCA. Analyze complex datasets to identify key areas for improvement in environmental
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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temporal properties: ultrabroadband supercontinua, intense sub-cycle field transients, and few-femtosecond ultraviolet pulses, among many others. We combine numerical modelling with experiments to study the
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plan to use these data to identify the virus and make inferences about potential human infection and transmission. This will involve analysis of viral evolution, simulation of potential scenarios and
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) an application focus in radio astronomy or magnetic resonance imaging, including both simulation and real data processing expertise. Competence in specific algorithmic approaches such as plug-and-play methods and