Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; Swansea University
- University of Nottingham
- ; The University of Manchester
- ; University of Birmingham
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Newcastle University
- ; University of Leeds
- ; University of Southampton
- ; University of Sussex
- ; University of Warwick
- AALTO UNIVERSITY
- University of Newcastle
- ; Imperial College London
- ; Loughborough University
- ; University of Bristol
- ; University of Exeter
- ; University of Nottingham
- ; University of Plymouth
- ; University of Reading
- Harper Adams University
- Imperial College London
- 13 more »
- « less
-
Field
-
sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
-
environmental inputs, algae physiological parameters and microbial community eDNA data to develop predictive mechanistic models which can be utilised to develop an optimal cultivation strategy. The project is
-
still hampered by: Ability to detect areas along the intertidal for optimal restoration3. Knowledge on how positive species interactions can be harnessed for rapid restoration4. Availability of devices
-
reducing waiting lists. This will be achieved through the following objectives: Acquire data and expert-based evidence and optimise data augmentation to ensure optimal hospital patient pathways through pre
-
modelling framework multiple ML tasks as mentioned above, to ease the development burden from users. It will research unified and modular modelling strategies, capable of optimally fusing and aligning diverse
-
reliability and operational efficiency. Determining the optimal size and location of PSTs within a network is inherently complex due to the nonlinear and dynamic nature of power systems, necessitating the use
-
, scalability, and adaptability to various applications such as autonomous systems, IoT devices, and wearable technologies. Research Focus Areas: 1- Neuromorphic and AI-Optimized Processors: Design AI-specific
-
the development of system software. Key questions include how LLMs can support programmers in writing complex logical code, generating high-quality tests, and optimizing performance. Moreover, when integrated with
-
to optimize metagenomic workflows across sample types, developing integrated, sample-specific methodologies. Collaborating with leading academic developers and front line metagenomics users, including
-
on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine