Real world datasets are often plagued by label and input noise due to variability in data collection, annotation errors, and incomplete records. Fully curating such datasets is costly and impractical, and models trained only on idealised or clean data often fail to generalise to other test sets in deployment. This project develops noise‑resilient AI models by jointly learning low‑dimensional representations for both the data and model parameters within the training phase to build models that learn from both aleatoric and epistemic uncertainty and become robust and generalisable. The project is in close collaboration with the National Physical Laboratory and benefits from the scientific environment and resources provided by the Centre for Vision, Speech and Signal Processing (CVSSP) and the Institute for People‑Centred AI at the University of Surrey.
About the Role
A generous stipend is offered in addition to funding for UK‑level tuition fees and research training.
Qualifications
* Degree in Computer Science, Mathematics, Physics, or Engineering.
* Prior experience in AI is necessary.
* Prior experience in tomographic imaging and medical physics would be advantageous but is not required.
* First Class undergraduate degree or MSc with Distinction (or equivalent overseas qualification) in mathematics, computer science, physics or engineering.
* Excellent mathematical, analytic, and programming skills.
* Previous experience in tomographic imaging would be advantageous.
Additional Information
Link to application – Robust Low‑Dimensional Representations for Noisy Real‑World Data at University of Surrey on FindAPhD.com.
For further information, please contact Spencer Thomas, Principal Scientist at NPL.
Employer & Diversity
National Physical Laboratory (NPL) is a world‑leading centre of excellence that provides cutting‑edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. NPL and DSIT are committed to diversity, equality of opportunity, and welcome applications from all backgrounds. All disabled candidates who satisfy the minimum criteria will be guaranteed an interview under the Disability Confident Scheme. We strive to nurture and respect individuals to ensure everyone feels valued and supported.
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