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surveillance) sensors can also be seen as temporal events. While data from current sensors can be manually converted into events for fast processing, it is also possible to develop hybrid structures where some
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platforms at our prestigious Centre for in vitro Predictive Models (https://www.cpm.qmul.ac.uk/ ), and work with project partners based at the Cross Institute Advanced Tissue Engineering (CREATE) lab
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cancer progression, immune evasion, and therapeutic resistance. We place a strong emphasis on the use of spatial biological approaches applied to human tumour models including organ/tumour perfusion, slice
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London, with a team of investigators covering AI, computer vision, robotics, and medical imaging. You will join a dynamic and successful team with access to both cutting-edge computer power and advanced
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Communication, Collaboration, and Care Delivery). This role focuses on Work Package 1 (WP1): Analysing the Spatial Design of Emergency Departments (EDs) in the Swiss Healthcare System using advanced spatial
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is focussed on understanding the human tumour microenvironment (TME) and its role in cancer progression, immune evasion, and therapeutic resistance. We place a strong emphasis on the use of advanced
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
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skills Knowledge of university processes and structures Experience abroad Experience in research in a medical context/with clinical populations Experience in research with digital interventions Strong
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. By combining advanced AI recognition with live inventory monitoring, FabricFlow addresses long-standing inefficiencies in supply chains caused by manual processes, disconnected data, and lack