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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time
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to optic nerve degeneration. Our research spans cutting-edge imaging technologies, drug discovery from small molecules to gene therapy, metabolomics, and experimental models ranging from human neurons
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute to competitive grant proposals and research impact activities
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cancer therapies, including gene- and cell-based immunotherapies. You will work with state-of-the-art technologies such as single-cell multiomics, stem cell models, and nanotechnology, within a
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responsibilities will be to: develop advanced modelling, novel material synthesis, processing, fabrication and manufacturing sequence, advanced characterisation and measuring methods for high performance perovskite
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applications of data science and modelling. The successful candidate will support the research of Professor Lucy Marshall, Faculty of Engineering and will collaborate with members of her cross-institutional
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interdisciplinary research efforts process and analyse functional neuroimaging data (fMRI, EEG, etc.) to extract meaningful insights into brain function. About you a PhD in Neuroscience, Computational Neuroscience
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Researcher in FPGA-based AI Hardware Acceleration who has: strong experience in FPGA design, machine learning or a related field in the case of the Postdoctoral Research Associate, a PhD (or near completion
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, stem cell models, and nanotechnology, within a collaborative and innovative research environment. It is an exciting opportunity to be at the forefront of translational cancer research. Key