486 computational-physics-"https:"-"https:"-"https:"-"https:"-"CESBIO" PhD positions in United Kingdom
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are invited for a fully funded Industrial Doctoral Landscape Award in partnership with Siemens Digital Industry Software, focused on advancing the next generation of industrial Computational Fluid Dynamics (CFD
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-generation regenerative materials. This interdisciplinary project combines mechanical, materials, and biomedical engineering, offering training across fabrication, nanomechanical analysis, and computational
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knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
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and associated events, alongside the formation of a novel multi-physic digital twin to support future forensic identification of UAS fingerprint profiles. The main objectives are: 1. Identify, evaluate
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of industry and academic project partners covering structural biology; biophysical and analytical methods; computational modelling; directed evolution; process modelling and development; digital skills
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Experience in sustainable architecture or building physics A strong interest in retrofit research Confidence in using quantitative methods, including environmental monitoring and performance evaluation
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of components. These WELs are regions of nano-grained material that are believed to form from severe plastic deformation at the surface during the machining process. Similar white-etching features are also
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centre for the development of medical imaging, particularly MRI. Eligibility You must be a university graduate or expecting to graduate with a 1st class degree in engineering, physics, computer
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. The studentship is funded by the Leverhulme Trust through the Connected Waters Leverhulme Doctoral Programme. Urban blue networks, including rivers, canals and wetlands, are dynamic systems that shape how cities
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(particularly cognitive or applied psychology) Cognitive Science Human–Computer Interaction Engineering or Computer Science Health sciences Experience in empirical research, experimental design, data analysis