Sort by
Refine Your Search
-
Listed
-
Employer
- Cranfield University
- ;
- University of Nottingham
- ; The University of Manchester
- ; City St George’s, University of London
- ; University of Exeter
- ; University of Nottingham
- ; Newcastle University
- ; Swansea University
- The University of Manchester
- ; University of Birmingham
- ; University of Bristol
- ; University of Leeds
- ; University of Southampton
- ; University of Surrey
- Abertay University
- ; Brunel University London
- ; Coventry University Group
- ; Cranfield University
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; The University of Edinburgh
- ; UCL
- ; UWE, Bristol
- ; University of Greenwich
- ; University of Oxford
- ; University of Reading
- ; University of Strathclyde
- ; University of Warwick
- Harper Adams University
- King's College London
- Oxford Brookes University
- UCL
- University of Birmingham
- University of Bristol
- University of Nottingham;
- University of Oxford
- University of Sheffield
- University of Warwick
- 29 more »
- « less
-
Field
-
treatment processes through advanced machine learning, validated against physics-based models and experimental data. 2. System Integration: Integrating the DTs into material and energy balance equations
-
, the adoption of AI in project management remains in the nascent stages. This PhD project will critically investigate how AI is shaping the management of projects and project studies. It aims to generate new
-
behaviours? The proposed approach will focus on developing a multi-agent AI framework that integrates traditional penetration testing methodologies with machine learning techniques and advanced generative AI
-
epidemiology and machine learning. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional support for research expenses
-
This PhD project aims to advance Safe and Sustainable by Design (SSbD) pharmaceutical manufacturing by integrating cutting-edge methodologies, including computer-assisted retrosynthesis, end-to-end
-
, adaptive control strategies, and hybrid energy storage solutions to address key challenges in self-powered systems under dynamic environmental conditions by: Develop machine learning or heuristic-based
-
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
-
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
-
specific drug resistance and pathogenesis mutations. The project will combine classical microbial genomics with machine learning and AI analysis approaches to create the most in depth population analysis
-
formats available in conventional hardware are often too accurate for the needs of machine learning: they do not improve the quality of the trained model but may deteriorate it by causing overfitting