Robust Low-dimensional Representations For Noisy Real-world Data
NPL Management Ltd
Surrey, United Kingdom
On-site
Low-dimensional representations
Noise-resilient ai models
Aleatoric and epistemic uncertainty
NPL Management Ltd is seeking candidates for a project focused on developing noise-resilient AI models for real-world datasets, which face challenges from label and input noise. Ideal applicants should have a degree in Computer Science, Mathematics, Physics, or Engineering, along with experience in AI; knowledge of tomographic imaging and medical physics is a plus
Job Summary
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.
A generous stipend is offered in addition to funding for UK-level tuition fees and research training.
Matching Summary
Match Score: 85
NPL Management Ltd is seeking candidates for a project focused on developing noise-resilient AI models for real-world datasets, which face challenges from label and input noise. Ideal applicants should have a degree in Computer Science, Mathematics, Physics, or Engineering, along with experience in AI; knowledge of tomographic imaging and medical physics is a plus.
Skills & Requirements
Must-have
low-dimensional representations
noise-resilient AI models
aleatoric and epistemic uncertainty
robust and generalisable models
Nice-to-have
tomographic imaging experience
medical physics experience
Key Requirements
Degree in Computer Science, Mathematics, Physics, or Engineering