Recent advances in computational imaging have demonstrated the benefits of deep-learning/data-driven methods to reconstruct images from highly noisy and sparse data. However, one of the main current bottlenecks is the lack of efficient and robust methods able to provide reliable measures of uncertainty about the information extracted. Such measures are particularly crucial for subsequent post-processing and automated decision-making. Statistical methods are preferred tools for uncertainty quantification as they allow data to be combined with additional information potentially available within a formalised, yet flexible, mathematical framework. The aim of this project is to propose new approximate methods leveraging scalable optimization and machine/deep learning engines for robust estimation in high-dimensional and dynamic problems. This project will aim at scaling methods such as expectation-propagation, variational Bayes and message passing tools to handle uncertainties and correlations in large-scale imaging problems.
All applicants must have or expect to have a 1st class MChem, MPhys, MEng, MSci or equivalent degree by Summer 2023. Selection will be based on academic excellence and research potential, and all short-listed applicants will be interviewed (in person or via Teams).
Closing Date: 9 of February 2023. All successful candidates must commence studies as soon as possible.
How to Apply
Apply: https://hwacuk.elluciancrmrecruit.com/Apply/Account/Login?ReturnUrl=%2fApply
When applying through the Heriot-Watt on-line system please ensure you provide the following information:
(a) in ‘Study Option’
You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Bio-engineering and Bio-sciences PhD/Chemistry PhD/Physics PhD/Electrical PhD/ Mechanical Engineering PhD for study option
(b) in ‘Research Project Information’
You will be provided with a free text box for details of your research project. Enter Title of the project for which you are applying and also enter the supervisor’s name.
This information will greatly assist us in tracking your application.
For questions about the application process, please contact [Email Address Removed]
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