Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ;
- ; Swansea University
- ; The University of Manchester
- University of Nottingham
- University of Cambridge
- AALTO UNIVERSITY
- University of Sheffield
- ; Cranfield University
- ; Brunel University London
- ; University of Birmingham
- ; University of Bristol
- ; University of Surrey
- Imperial College London
- ; City St George’s, University of London
- ; The University of Edinburgh
- ; University of Cambridge
- ; University of Sheffield
- ; University of Southampton
- ; University of Sussex
- Abertay University
- ; Coventry University Group
- ; Durham University
- ; Loughborough University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; University of Copenhagen
- ; University of Greenwich
- ; University of Nottingham
- ; University of Oxford
- ; University of Strathclyde
- ; University of Warwick
- Aston University
- Nature Careers
- UNIVERSITY OF SOUTHAMPTON
- UNIVERSITY OF VIENNA
- University of Manchester
- University of Newcastle
- 28 more »
- « less
-
Field
-
heavier than their fossil fuel powered counterparts. A framework that can accurately model complex dynamics and generate projections for future scenarios is essential for understanding the impact of changes
-
, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods. It is also essential that you are highly experienced in setting up continuous
-
of Computational Solid Dynamics Building upon very recent work made by the supervisory team, this FULLY FUNDED 4-year PhD project will investigate challenging aspects such as: (1) exploration of a truly “n” multi
-
PhD Application". Emails should arrive no later than 1 September 2025. Applications may close early if the position is filled before this date. Please note that any offer of funding will be conditional
-
supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
-
The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence
-
Description: This PhD project offers an exciting opportunity to co-develop and pilot a systems-based, interdisciplinary framework for circularity assessment in the water sector. Working closely with Anglian
-
is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems and their infrastructure. This PhD project aims to enhance
-
development and refinement accordingly. We are looking for a highly organised, driven, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods
-
. Applications may close early if the position is filled before this date. Please note that any offer of funding will be conditional on securing a place as a PhD student. Candidates will need to apply separately