Research Associate

The University of Bath seeks a Research Associate in Mathematical Sciences to conduct theoretical analysis and numerical implementation of operator learning for gradient flows. Key responsibilities include close collaboration with Doshisha University, Japan, and participation in research visits. Applicants must hold a PhD in mathematics or a related field and possess experience in optimization, inverse problems, PDEs, or operator learning.

• 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 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 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.