Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; University of Warwick
- ; The University of Manchester
- University of Nottingham
- ; University of Exeter
- ; University of Nottingham
- ; University of Leeds
- ; Newcastle University
- ; University of Oxford
- ; University of Surrey
- ; Cranfield University
- ; Manchester Metropolitan University
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Bristol
- ; University of Reading
- ; University of Southampton
- Abertay University
- Imperial College London
- University of Cambridge
- ; Aston University
- ; Brunel University London
- ; City St George’s, University of London
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; Queen Mary University of London
- ; Swansea University
- ; UCL
- ; UWE, Bristol
- ; University of Copenhagen
- ; University of East Anglia
- ; University of Greenwich
- ; University of Strathclyde
- ; University of Sussex
- Harper Adams University
- Newcastle University
- University of Liverpool
- University of Newcastle
- University of Sheffield
- 31 more »
- « less
-
Field
-
, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in
-
for research into thermal management and system health monitoring, supporting studies in military aircraft systems. Engaging with these facilities allows students to acquire practical skills and technical
-
This forward-looking PhD project merges performance science with advanced data analytics and machine learning to further enhance performance prediction in elite rugby union. The successful candidate
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. •Specialist training in AI, machine learning, and digital engineering. •Collaboration with academic and industry experts for technical insight and mentoring. •A supportive research environment focused on both
-
. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
-
of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging
-
computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and
-
to begin focused research early on. To complement their background, optional training in natural language processing, machine learning, and linguistics will be available. During the first year, the student
-
configurations. Machine learning techniques will be incorporated to dynamically adjust PST settings in response to evolving grid conditions. This multi-layered approach aims to bridge the gap between static
-
. Applying machine learning to New Zealand’s landslide inventories to model landslide location, character and dynamics. Integrating time-series and inventory data to develop new models to predict location