Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- ; The University of Manchester
- Cranfield University
- ; Swansea University
- ; Cranfield University
- ; University of Birmingham
- University of Nottingham
- ; University of Nottingham
- University of Sheffield
- ; University of Warwick
- ; University of Bristol
- Newcastle University
- ; Newcastle University
- ; The University of Edinburgh
- ; University of Exeter
- ; University of Oxford
- ; University of Southampton
- ; University of Strathclyde
- ; University of Surrey
- ; University of Sussex
- Abertay University
- Harper Adams University
- Imperial College London
- University of Cambridge
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; Midlands Graduate School Doctoral Training Partnership
- ; UWE, Bristol
- ; University of East Anglia
- ; University of Greenwich
- ; University of Leeds
- ; University of Plymouth
- ; University of Portsmouth
- ; University of Sheffield
- ; Xi'an Jiaotong - Liverpool University
- University of Oxford
- 27 more »
- « less
-
Field
-
environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
-
. Experimental studies will be performed in wind tunnels with advanced measurement techniques with high spatial and temporal resolutions. Realistic car models (DrivAer models) will be considered in this study and
-
the understanding of offshore turbulence in spatially varying flows. The focus will be on open channel flow dynamics and controlled experimental studies will be designed and conducted to generate and characterise
-
and develop advanced cryogenic power electronics solutions for key net-zero applications such as all-electric aviation and wind energy. This fully-funded PhD project will provide the opportunity
-
on these comparisons, you will create agent-based models (ABMs) that define interaction rules based on observed similarities and differences in events [4], with a focus on the specific role of individual differences
-
machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
-
desorption mechanisms of various FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property
-
intestinal disease (IID) in nurseries. As disease hotspots, current approaches often fail. The Problem: IIDs like Shigatoxigenic Escherichia coli (STEC) can cause serious illness in children. Existing
-
It is imperative to use well characterised, human-relevant in vitro models to assess air pollution exposure impact upon human health, and how it may develop disease. However, despite the known
-
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