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The University of Bath invites applications for a Research Associate to work on operator learning for gradient flows with applications in imaging. The role sits within a collaboration with Doshisha University, Kyoto, with opportunities for regular research visits to Japan.
A PhD in mathematics or related field is required, with emphasis on convex analysis, inverse problems, and PDEs. You will engage in theoretical analysis and numerical implementations, and contribute to the project’s
The role is associated with the Royal Society International Collaboration Award “Operator learning for gradient flows with applications in imaging” led by Dr Yury Korolev (University of Bath) and Dr Takashi Furuya (Doshisha University, Kyoto). Close collaboration with Doshisha University is envisaged and the successful candidate is expected to take part in regular research visits to Japan. The project concerns theoretical analysis and numerical implementation of operator learning methods for gradient-flow-type equations driven by maximally monotone operators.
The role is associated with the Royal Society International Collaboration Award “Operator learning for gradient flows with applications in imaging” led by Dr Yury Korolev (University of Bath) and Dr Takashi Furuya (Doshisha University, Kyoto). Close collaboration with Doshisha University is envisaged and the successful candidate is expected to take part in regular research visits to Japan. The project concerns theoretical analysis and numerical implementation of operator learning methods for gradient-flow-type equations driven by maximally monotone operators.
As a member of Research Staff at the University of Bath, you will be encouraged to take up a minimum of 10 days professional development pro rata per year.
A lower grade offer may be made (Grade 6) with comparable reduction in responsibilities and amendment in job title to Research Assistant, if a suitable applicant cannot be found to fill the Grade 7 position. Applicants will need to be within six months of their course completion date and will be promoted to Research Associate once their PhD has been awarded.
The successful candidate will have a PhD (or equivalent) in mathematics or a closely related discipline and have experience in at least one of the following areas (or a closely related area): convex analysis and optimisation, inverse problems, calculus of variations, partial differential equations, operator learning, functional analysis.
For an informal discussion regarding the role please contact Yury Korolev at ymk30@bath.ac.uk. We do not require services of recruiting or advertising agencies, please refrain from contacting us.
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