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
- 2022-2025, Optimisation of AI for cancer screening lead for the University of Surrey in collaboration with National Physical Laboratory Data Science and Medical Physics, and the Royal Surrey NHS Foundation Trust Scientific Computing
- 2023-2024, Data analytics and AI for optimised radiation detection principal investigator for the University of Surrey and part of the NuSec Sigma Data Challenge
- 2023-2026, Automated Decision Making to Streamline Radioisotope Identification
- 2023-2027, Optimisation of radiation health monitoring through machine learning and data fusion part of NTRnet
- 2022-2025, Enhanced nuclear fuel monitoring, part of the supervisory team in collaboration with the National Physical Laboratory
Past projects
- Machine learning techniques for nuclear decommissioning lead in collaboration with the University of Surrey Chemistry Department, National Nuclear Laboratory and part of the TRANSCEND consortium | 2019-2023
- Optimised scintillator section through fuzzy logic principal investigator for the University of Surrey in collaboration with the Nuclear Science Security Network | 2021
- Quantum dot enhanced detection technology principal investigator for the University of Surrey | 2017-2021
- Innovative alpha detection for environmental applications principal investigator for the University of Surrey using Airthings Radon detector | 2018-2019
- Thermoluminescence of fibres and beads for radiation dosimetry lead for the University of Surrey | 2018-2022
PhD student projects
- L Tomaszewski, started 2024, Optimisation of radiation health monitoring through machine learning and data fusion
- J Wroe-Brown, started 2023, Automated Decision Making to Streamline Radioisotope Identification.
- A Worthy, started 2022, Development and use of new and derived data to facilitate the optimisation and evaluation of breast screening AI tools.
- A Hickman, started 2022, Assessing the reliability of AI for predicting the risk of breast cancer.
- L Lee-Brewin, 2019-2023, Machine Learning Techniques Applied to Challenging Gamma Spectra https://doi.org/10.15126/thesis.901067
- C Termsuk, 2018-2022 Silica based fibres for radiation dosimetry https://doi.org/10.15126/thesis.900471
- C Grove, 2017-2021 Quantum Dot Loaded Nanocomposite Plastic Scintillators, https://doi.org/10.15126/thesis.900111
- K Ley, 2016-2020, Thermoluminescence of silica beads for dosimetry up to 250 kGy, formerly with Dr Lohstroh https://doi.org/10.15126/thesis.00853311
- S Parsons Detecting ionizing radiation with polarising light (completed 2017) Industry supervisor https://openresearch.surrey.ac.uk/esploro/outputs/99511485702346
General topics for future UG, MSc and PhD projects
- Fuzzy logic for nuclear applications
- Convolutional neutral networks applied to Nuclear Security
- Machine leaning within Nuclear Medicine
- Use of automatic AI code generation for the Nuclear Industry
- AI Deep Learning methods
- Genetic algorithms, sparse data techniques
Get in contact
If you would like further information about our research then please contact Dr Caroline Shenton-Taylor.