Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ; City St George’s, University of London
- AALTO UNIVERSITY
- ; Swansea University
- University of East Anglia
- University of Nottingham
- University of Sheffield
- KINGS COLLEGE LONDON
- University of Bristol
- University of Sheffield;
- University of Surrey
- ; Brunel University London
- ; Coventry University Group
- ; Newcastle University
- ; The University of Manchester
- ; University of Exeter
- ; University of Southampton
- Abertay University
- Harper Adams University
- King's College London;
- Manchester Metropolitan University
- The University of Edinburgh
- The University of Manchester
- The University of Manchester;
- UCL
- University of Birmingham
- University of Birmingham;
- University of Cambridge
- University of Cambridge;
- University of East Anglia;
- University of Exeter
- University of Newcastle
- University of Nottingham;
- University of Oxford
- 24 more »
- « less
-
Field
-
machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
-
mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
-
for their employability in applications. Additionally, machine learning methods need to be applicable to high-dimensional and to noisy data that are typically encountered in real-world applications. The aim of this project
-
on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
-
change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems. At the forefront of this innovation is machine learning. Its ability to process complex
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
will train machine learning models to identify and assess internal defects with greater accuracy and speed than traditional methods. The results will support predictive maintenance, reduce inspection
-
, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In
-
configurations and illumination conditions. Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize
-
intelligence, NLP, machine learning, or a related field Experience with Python and Generative AI libraries (e.g., Huggingface Transformers) Knowledge of Multimodal Generative AI models and their corresponding
-
? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity