Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- Cranfield University
- ; Swansea University
- University of Manchester
- ; The University of Edinburgh
- ; The University of Manchester
- University of Nottingham
- ; Newcastle University
- ; University of Birmingham
- ; City St George’s, University of London
- ; University of Cambridge
- ; University of Exeter
- ; University of Leeds
- ; University of Warwick
- University of Cambridge
- ; Brunel University London
- ; Cranfield University
- ; Loughborough University
- ; Manchester Metropolitan University
- ; University of Bradford
- ; University of Bristol
- ; University of Copenhagen
- ; University of East Anglia
- ; University of Nottingham
- ; University of Oxford
- ; University of Reading
- ; University of Sheffield
- ; University of Southampton
- ; University of Surrey
- Abertay University
- Harper Adams University
- 21 more »
- « less
-
Field
-
sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
-
-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
-
of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
-
leverage low-precision accelerators for scientific computing by using a number of tricks, known as "mixed-precision" algorithms. Developing such algorithms is far from trivial. We can look at computational
-
formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
-
aligning with NQTP Missions 1 and 2 and NQCC Testbed programme, will tailor the developed benchmarking approaches to error-corrected as well as distributed quantum computers, addressing the need for scalable
-
abort (or not engage) if the bright white lines that fit a defined and rigid expectation are not clearly visible. These systems use algorithms, rather than AI machine learning, to detect road markings and
-
algorithms, validated navigation architectures, and new insights into next-generation intelligent mobility solutions. The student will undertake two industry placements at Spirent, use high-tech simulation
-
enable a step change in power conversion, transmission and distribution through power electronics based on new materials. At the heart of such systems are power semiconductor devices. The advantages
-
, stress markers, EEG, and ECG — will be collected by VR headsets and IoT devices. ML algorithms will analyse this data to identify trends, project risk factors, and propose tailored treatments. By combining