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record relevant to career stage research expertise in one or more of the following: digital twins, cyberphysical systems, human-centred AI causal modelling, uncertainty quantification, explainable AI
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research in this or related fields proven expertise in metabolic studies using mouse models, including manipulating protein expression via AAV methods and generating high‑quality tissue proteomics datasets
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the following: digital twins, cyberphysical systems, human-centred AI causal modelling, uncertainty quantification, explainable AI, or trustworthy AI agentic AI, multiagent systems, sequential decision
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, signal processing and inverse modelling. The role is based at the Image X Institute and will be led by Dr Chandrima Sengupta. You will benefit from a strong mentoring environment; lead and be part of high
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scale-up modelling Integration of strain performance, sustainability, and techno-economic constraints You will work closely with strain engineers, students, and collaborators in a highly integrated team
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-culture, single-cell RNA sequencing, data analysis and in vivo models—to interrogate B cell responses in human samples and pre-clinical systems. This research aims to generate mechanistic insight
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well as the opportunities and challenges for future supply. Anticipated outcomes include an understanding of how the emergent policy context will shape student housing delivery and trace the potential for new models
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-aneutronic fusion energy reactor concepts. For HB-11 Energy, the results will directly inform the company’s technology roadmap and contribute to balance-of-plant modelling to assess economic viability
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or observing programmes Machine learning in physical science, especially with transformer models Jax, PyTorch, and/or Julia; with probabilistic programming languages; or with high-dimensional optimization and
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requirements definition, CAD/FEA modelling, multi-objective optimisation (e.g., DOE/GA/surrogates), prototype build, and laboratory validation. Track record of peer-reviewed publications (e.g., IEEE and IET