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Field
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You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria Hold a PhD or about to obtain a PhD degree in Computer
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This fully-funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA), Cranfield University and Spirent Communications, offers a bursary of £25,000 per annum, covering full
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form): Essential: PhD (or equivalent) in a relevant field (e.g., bioinformatics, computational biology, molecular biology, cell biology, biochemistry, developmental biology, genetics) Previous work
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
of interest, skills and previous experience, motivation for Doctoral Research, future goals, and why you are applying to this project. On the application system select Programme of Study – PhD
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development can alter tissue mechanical properties and allow cancer cells to withstand compressive forces within a tumour environment. This PhD project aims to map keratin network architecture in epithelial
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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more than 150 different countries. Degree programmes are provided in Arts and Humanities, Social and Historical Sciences, Education, Architecture, Building, Environmental Design and Planning, Laws, Life
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and industrial integration. • Provide expert-level technical and research support to the Architectural Engineering Group. • Contribute to ongoing research projects, funding applications
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lectures on the following modules: Analog Signals and Systems (45AH) Digital Signals & Systems (45AH) Machine Learning (45AH) Operating Systems/Computer Architecture (45AH) Computer Vision/Image Processing
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be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD awarded in Electrical or Computer Engineering Knowledge about Deep Learning