Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; University of Warwick
- ; University of Nottingham
- University of Nottingham
- ; The University of Manchester
- ; University of Exeter
- University of Cambridge
- ; Swansea University
- ; University of Leeds
- ; University of Oxford
- ; University of Reading
- ; University of Surrey
- ; Anglia Ruskin University
- ; City St George’s, University of London
- ; Cranfield University
- ; Durham University
- ; Loughborough University
- ; Newcastle University
- ; Queen Mary University of London
- ; UWE, Bristol
- ; University of Bristol
- ; University of Cambridge
- ; University of East Anglia
- ; University of Greenwich
- ; University of Hull
- ; University of Sheffield
- ; University of Southampton
- ; University of Strathclyde
- ; University of Sussex
- Imperial College London
- Newcastle University
- University of Birmingham
- University of Glasgow
- University of Sheffield
- 25 more »
- « less
-
Field
-
untapped potential remains in extracting value from this data. This PhD will explore advanced analytics techniques, including machine learning, digital twin modelling, time series analysis, spectral analysis
-
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
-
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
-
for individuals with a strong interest in artificial intelligence, machine learning, process systems engineering, and pharmaceutical manufacturing. The expected outcomes will contribute to more resilient
-
through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
-
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
-
. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims
-
. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. •Specialist training in AI, machine learning, and digital engineering. •Collaboration with academic and industry experts for technical insight and mentoring. •A supportive research environment focused on both