Sort by
Refine Your Search
-
Category
-
Employer
- Cranfield University
- ;
- ; The University of Manchester
- ; University of Leeds
- ; University of Warwick
- University of Newcastle
- ; Brunel University London
- ; Cranfield University
- ; Loughborough University
- ; Newcastle University
- ; Swansea University
- ; University of Nottingham
- ; University of Oxford
- ; University of Surrey
- Abertay University
- Imperial College London
- University of Nottingham
- 7 more »
- « less
-
Field
-
, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
-
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
-
shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
-
solutions need to be safe and accurate. Aim This project will focus on investigating and developing new ways in which deep learning-based solutions can continuously learn and deal with unseen situations, with
-
simulation regimes by harnessing and advancing the latest developments in AI Machine Learning. This studentship is a continuation of prior work that is looking at using new cutting-edge deep learning models
-
an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
-
understood how such automation solutions can be safely and robustly supported with state-of-the-art deep learning. There is a need for new AI that can incrementally learn and adapt without losing accuracy
-
., health and climate/environmental data) and could include a range of data science methods, such as utilising geographical information systems (GIS), statistical analysis, machine learning, deep learning
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
Fully funded PhD at Cranfield University, supported by the EPSRC DTP and Rolls-Royce. This 3-year project covers tuition fees, a tax-free stipend, and funding for training, conferences, and a
-
deep learning methods to enhance the predictions beyond existing data. By incorporating microstructural features into predictive models, the aim is to create a reliable data-driven modelling framework