Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Newcastle University
- University of Exeter
- University of Nottingham
- Cranfield University
- University of Surrey
- University of Warwick
- ;
- Aston University
- Imperial College London
- King's College London
- Swansea University
- The University of Manchester
- University of Birmingham
- University of Cambridge;
- University of Essex;
- University of Liverpool
- University of Newcastle
- University of Nottingham;
- University of Sheffield
- University of Strathclyde
- 10 more »
- « less
-
Field
-
). Overview This PhD will develop a data-driven framework to understand how climate-driven extreme weather affects electricity distribution networks and how targeted interventions can reduce disruption. Using
-
science or systems engineering. Knowledge of AI/ML algorithms, particularly graph neural networks and reinforcement learning, is highly advantageous. A keen interest in distributed computing, IoT architecture, and
-
to investigate the mechanisms of migration of these radioisotopes. All students funded through the RAPTOR DFA network will be required to undertake 60 credits of training modules as part of the PhD, which will be
-
), such as solar photovoltaics (PV), electric vehicles, heat pumps, and storage systems, into distribution networks. Delivering this transition requires coordinated innovation across both active distribution
-
distribution of benefits, and persistent injustices. This studentship will contribute to illuminating these challenges through rigorous, community‑centred, and interpretive research. The successful PhD candidate
-
Royal Academy of Engineering (RAEng) Research Chair on distributed radar systems. The project covers UK tuition fees and the standard UKRI PhD stipend and has an allowance for project-related expenses
-
Research area and project description: AI data centres are digital engines, yet ~30% of energy is wasted as heat in power conversion and distribution. Directly addressing the UK’s Clean Power 2030
-
: Design layered control architectures with sentry and reversionary functions that provably allow the system to "degrade gracefully". Experimental Validation: Demonstrate your methodology on a networked
-
The increasing prevalence of autonomous systems in dynamic, human-centred environments, such as smart transportation networks and distributed IoT infrastructures, demands decision-making frameworks
-
Mobile Edge Computing (MEC) has emerged as a promising computing paradigm to support emerging high-performance applications by deploying resources at the network edge. However, most existing MEC