Research Associate
Join a collaborative project on operator learning for gradient flows.
Best for:Ideal for mathematicians with a strong background in optimization and differential equations.
Overview
The University of Bath is seeking a Research Associate to work on a project funded by the Royal Society International Collaboration Award. This role involves theoretical analysis and numerical implementation of operator learning methods for gradient-flow-type equations, with a focus on collaboration with Doshisha University in Japan. The successful candidate will engage in regular research visits to Japan and is expected to contribute to the project's objectives. Applicants should be nearing the completion of their PhD in mathematics or a related field.
What you'll do
- Conduct theoretical analysis of operator learning methods.
- Implement numerical solutions for gradient-flow-type equations.
- Collaborate closely with researchers at Doshisha University.
- Participate in regular research visits to Japan.
- Engage in professional development activities.
Requirements
Degree required: PhD in Mathematics or a closely related discipline
Language: English; proficiency required
Instruction: English
- Experience in convex analysis and optimization
- Knowledge of inverse problems
- Familiarity with calculus of variations
- Understanding of partial differential equations
- Experience with operator learning
- Background in functional analysis
- Must be within six months of PhD completion
- Open to all nationalities
Funding & terms
How to apply
Deadline: 2026-11-16
Interested candidates should submit an up-to-date CV and a research statement of up to 2 pages. The research statement should outline previous work and how it aligns with the project.
- CV
- Research statement
Contact
Yury Korolev· Supervisor
Create a free profile to unlock your personal match score, the full requirements, funding details and how to apply — in English or Persian.