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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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of differential tumour release in ex vivo studies, in vitro and in vivo proof-of-concept in CRC resistance models will be evaluated. Main skills Medicinal chemistry; drug metabolism analysis; in vitro and in vivo
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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Are you passionate about cognitive computational neuroscience and eager to contribute to cutting-edge research in perception? We invite applications for a fully funded PhD studentship supported by
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as Aeronautic and Aerospace Engineering, Biomedical Engineering, Product Design and Engineering (a minimum honours degree at UK first or upper second-class level). Experience in computational modelling
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The project aims to develop a publicly accessible in silico database that identifies and catalogues bacterial genes capable of altering the effectiveness of chemotherapy drugs in colorectal cancer (CRC) patients. Focusing on microbial modification of 5-fluorouracil (5FU), the study will...
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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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Testing (NDT) – a crucial technique used in industries to assess materials, components, and structures without causing damage. This PhD project focuses on advancing Ultrasound Testing and X-ray Computed
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-Stokes equation are not eligible. Solving the BBGK equation is extremely challenging due to the high computational cost originated from its high-dimensionality. As a deterministic method, the potential