540 computer-"https:"-"APOS-UFFICIO-CONCORSI-DOCENTI" "https:" "https:" "https:" "https:" "U.S" positions at University of Sheffield
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
/ preparation of project and programme budgets, in collaboration with Research Engineers to ensure they are not exceeded and spent on relevant project items. Detailed management, control and reporting throughout
-
organise and deliver an annual programme of staff engagement events Build strong relationships with stakeholders across the University to plan, coordinate and develop communications and projects Provide
-
(REEs) creates significant global challenges. This project tackles this problem with an innovative, dual-pronged approach. This project will use a high-throughput computational search for entirely new
-
used, which is typically done for reasons of computational efficiency. The project can also include simulations where in-plane shear is acting on the potential crack plane. Funding Notes 1st or 2:1
-
. The PhD programme will address research challenges including: • Generalisation vs specialisation: How to learn common physical behaviour across different gas turbine engine types while adapting efficiently
-
recognition, and diarisation. About the School and Research Group You will be a member of the Speech and Hearing research group in the School of Computer Science at the University of Sheffield and an
-
, their simplifying assumptions limit accuracy when dealing with modern reactor design and complex safety scenarios. Conversely, high-fidelity Computational Fluid Dynamics (CFD) offers immense detail but remains
-
Characterization and convergence of non-Brownian webs School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Nic Freeman Application Deadline: Applications accepted all year round Details The Brownian web is a dense system of coalescing Brownian motions in 1+1...
-
Organic quantum batteries: the development of high-performance energy storage devices (S3.5-MPS-Lidzey)
-
Digitalising populations of structural systems using machine learning (S3.5-MAC-Dardeno)