Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ; City St George’s, University of London
- KINGS COLLEGE LONDON
- ;
- ; Coventry University Group
- ; Swansea University
- ; University of Birmingham
- ; University of Plymouth
- Abertay University
- Harper Adams University
- Loughborough University;
- The University of Manchester;
- University of Cambridge;
- University of Newcastle
- University of Nottingham
- University of Nottingham;
- 6 more »
- « less
-
Field
-
engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
-
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
-
Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Behaviour-Based Anomaly Detection
-
Early and accurate cancer detection is a major global healthcare challenge, with significant implications for patient outcomes and treatment strategies. Time-of-Flight Positron Emission Tomography
-
neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
-
Almost all radar systems currently transmit from the same location. This kind of radar modality has been optimised for decades, however the challenge to detect very small and very fast objects
-
distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By establishing an understanding of "what normal looks like", these models will detect deviations from the norm and
-
. The enhanced image quality will support earlier and more reliable detection of eye diseases. Combining artificial intelligence with mathematical modelling, this non-invasive, cost-effective approach has
-
establishing an understanding of "what normal looks like", these models will detect deviations from the norm and effectively identify potential anomalies during testing. You will explore both self-supervised
-
services would be to keep children close to their usual home (where safe to do so) and maintain their family and community networks often this is not possible and high cost out of area placements