PhD Studentship - Data-Driven Computational Sensing and Imaging

Today's state-of-the-art imaging and sensing rely as much on computation as they do on sensor hardware. Furthermore, Computational Sensing and Imaging is increasingly exploiting data-driven and machine learning solutions to enhance performance and develop novel hardware/software co-designed sensing systems. However, in defence scenarios it is vital that verifiable algorithmic solutions are used, which places restrictions on which machine learning approaches are admissible. Importantly, fully black box machine learning solutions should be avoided. This project will therefore focus on the development of novel algorithmic and mathematical frameworks to exploit data and machine learning for imaging and sensing within a controlled explainable and verifiable manner. There will be a specific focus on RF and electro-optic/IR sensor modalities. This project will consider a range of algorithmic and machine learning technologies including: low rank models and/or auto-encoder type architectures to identify low dimensional data representations; physics-informed and physics aware neural networks that ensure the machine learning solutions adhere to necessary physics within the sensing problem; machine learning solutions targeted reducing computation or processing time; robustness to noise, outliers and adversarial attacks; and Bayesian and variational architectures that can provide uncertainty quantification. This project will be jointly supervised by: Prof Mike Davies, Mike.Davies@ed.ac.uk Prof James Hopgood, James.Hopgood@ed.ac.uk Please note that this advert will close as soon as a suitable candidate is found. Eligibility: Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Please note that as this is a defence based project, only UK/EU students are eligible to apply. International applicants are not eligible. Further information on English language requirements for EU/Overseas applicants. Funding: Tuition fees + stipend are available for applicants who qualify as: a UK applicant an EU applicant (International/non EU students are not eligible) Funding is available through EPSRC Prosperity Partnership Programme. As this is a defence related project there are nationality restrictions (see above). Informal Enquiries: Prof Mike Davies, mike.davies@ed.ac.uk Further Information:  The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity