Applied nuclear physics
Woking closely with industry, we apply and develop computational and experimental methods to detect radioactive and nuclear material across security, energy and medical applications. Our research draws on the latest artificial intelligence approaches including convolutional neural networks, wavelet analysis, genetic algorithms and fuzzy logic.
Current research projects
Optimisation of AI for breast screening
In collaboration with National Physical Laboratory Data - Science and Medical Physics, and the Royal Surrey NHS Foundation Trust Scientific Computing.
Data analytics and AI for optimised radiation detection
Research member of the NuSec Sigma Data Challenge.
Machine learning techniques for nuclear decommissioning
In collaboration with te Chemistry Department, National Nuclear Laboratory and part of the TRANSCEND consortium.
Enhanced nuclear fuel monitoring
In collaboration with the National Physical Laboratory.
Past projects
- Optimised scintillator section through fuzzy logic | 2021
- Quantum dot enhanced detection technology | 2017-2021
- Innovative alpha detection for environmental applications | 2018-2019
- Thermoluminescence of fibres and beads for radiation dosimetry | 2018-2022.
PhD student projects
- Development and use of new and derived data to facilitate the optimisation and evaluation of breast screening AI tools, A. Worthy (started 2022)
- Assessing the reliability of AI for predicting the risk of breast cancer, A. Hickman (started 2022)
- Machine Learning Techniques Applied to Challenging Gamma Spectra, L. Lee-Brewin (started 2019)
- Silica based fibres for radiation dosimetry, C Termsuk (completed 2022)
- Quantum Dot Loaded Nanocomposite Plastic Scintillators, C. Grove (completed 2021)
- Thermoluminescence of silica beads for dosimetry up to 250 kGy, K. Ley (completed 2020)
- Detecting ionizing radiation with polarising light (completed 2017).
Get in contact
If you would like further information about our research then please contact Dr Caroline Shenton-Taylor.