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Field
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vapour deposition (CVD) remains a leading method for CNT synthesis, yet the role of catalyst composition in controlling CNT morphology and purity is still not fully understood. This PhD project, in
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You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
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applications, facilitating the transition of image-based measurement methods from laboratory research to clinical practice. Digital Image Correlation (DIC) is a well-established, non-contact optical technique
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of the Hub. Our approach enhances T2 (Interconnected QC systems) through verification methods for connected networks, supports T1 (Integrated quantum demonstrators) via hardware-agnostic metrics, and enables
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with UKAEA, providing opportunities for engagement with leading fusion research facilities. Together, they offer a wealth of experience in integrating simulation and experimental methods to solve real
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
; University of Sussex | The City of Brighton and Hove, England | United Kingdom | about 1 month agoto: Informal enquiries about the project can be made to Georgios Giamas g.giamas@sussex.ac.uk and Erika Mancini Erika.mancini@sussex.ac.uk How to apply: Please submit a formal application using the online
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development and refinement accordingly. We are looking for a highly organised, driven, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods
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given a verbal offer and once it has been accepted, will be sent a formal offer letter and, in due course, a registration pack with joining information. Unsuccessful candidates will be contacted with
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-driven ML methods. Applicants should have, or expect to achieve, at least a first honours degree or a master’s with distinction (or international equivalent) in a relevant science or engineering related
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems