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platform , published in Nature Communications (https://www.nature.com/articles/s41467-020-18059-7), that enables localized, hyper-efficient delivery of therapeutic compounds to specific brain regions
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motivated PhD student interested in research in upper limb marker less motion capture, movement classification, outcome modelling in stroke populations, and synthetic data evaluation. The position offers
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problems. Project background The increasing availability of multimodal health data, coupled with advances in AI, offers new opportunities to deliver personalised, scalable behavioural interventions in
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) relevant to energy, process, and materials engineering, with a strong focus on: Advanced numerical modelling and simulation Experimental methods and sensor technologies Data analysis of real-world
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, growth and mechanics. Our group combines quantitative imaging data with mathematical and computational models to understand how tissues self-organise and how this process goes wrong in disease. We
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set of field datasets The collected data includes timelapse point clouds, video imagery, as well as auxilary data including environmental parameter timeseries This data is then processed to derive
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-relevant questions. We work with macroeconomic, microeconomic, firm-level, household, geospatial and international datasets and apply modern econometric methods, including panel-data, time-series and causal
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content before it is published. Research at the Center builds on the group's track record in data science, conversational AI, and marketing analytics and on substantial investments in on-premises GPU
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, vehicle, and health data with immersive eXtended Reality (XR) experimentation and explainable Artificial Intelligence to analyse safety-critical situations that are rare, underreported, or ethically
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problem: nonlinear, high-dimensional models with uncertain parameters that must be fitted to noisy data, and simulations so expensive that fitting them demands new algorithms and scalable software