Sort by
Refine Your Search
-
Listed
-
Employer
- Cranfield University
- ;
- ; Swansea University
- ; University of Warwick
- University of Cambridge
- ; University of Exeter
- ; Durham University
- ; Edge Hill University
- ; Newcastle University
- ; University of Bristol
- ; University of Surrey
- University of Nottingham
- ; Aston University
- ; Brunel University London
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; The University of Manchester
- ; University of Birmingham
- ; University of East London
- ; University of Gloucestershire
- ; University of Greenwich
- ; University of Reading
- ; University of Southampton
- ; University of Sussex
- UNIVERSITY OF EAST LONDON
- University of East London
- 16 more »
- « less
-
Field
-
easier tracking of applications. Funding Fully and directly funded for this project only for 3.5 years (total £137k): Standard UKRI stipend for 42 months (currently £20,780 per year, tax free) Tuition fees
-
to the development of digital twin technologies for sCO2 power generation systems. The Centre for Propulsion and Thermal Power Engineering has a key focus and a proven track record on gas turbine performance, gas path
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
-
is still to materialise, with one of the key barriers being its cyber security and privacy concerns. These concerns include data security, user privacy, and the potential manipulation or tracking
-
to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom. Entry requirements A minimum of a 2:1 first degree in a
-
and Paralympic games cycles where the cycling team have seen unparalleled success on the track. We are pleased to be able to offer a PhD opportunity working alongside Team GB cycling and Shell to
-
. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
-
a fast-track career in Wind Turbine Industry that is allied to an environmentally sustainable energy future, or academic career in the aerodynamics, fluid mechanics and aeroacoustics fields. Joining
-
, characterisation of the anisotropic response of composite systems, and material processing (including novel additive manufacture). These challenges are highly complex, and the relative experience and track record
-
publication-oriented, allowing you to build a strong academic and professional track record in energy-efficient and secure AI hardware innovation. By the end of this PhD, candidates will possess advanced