Research Associate: Operator Learning for Gradient Flows

The Royal Society; Newton St. Loe; On-site; GBP 39,000 - 46,000; Full time

Benefits offered by this job

Generous annual leave

Pension scheme

Cycle to work scheme

Relocation allowance

Visa reimbursement

Job summary

University of Bath invites applications for a Research Associate (SS13981) to work on operator learning for gradient flows with applications in imaging. The role includes theoretical analysis and numerical implementation, with regular research visits to Japan and collaboration with Doshisha University, Kyoto.

Applicants should have a PhD in mathematics or a related field and a strong record in convex analysis, optimization, or related areas.

Qualifications

• PhD in mathematics or closely related field required.
• Experience in convex analysis, optimisation or related areas desirable.
• Strong background in PDEs, inverse problems or operator learning.

Responsibilities

• Theoretical analysis and numerical implementation of operator learning methods for gradient-flow-type equations.
• Collaborate with international partners and participate in research visits as required.

Skills

Operator learning

Convex analysis

Optimization

Inverse problems

Calculus of variations

Partial differential equations

Functional analysis

Education

PhD in mathematics or closely related discipline

Job description

University of Bath invites applications for a Research Associate (SS13981) to work on operator learning for gradient flows with applications in imaging. The role includes theoretical analysis and numerical implementation, with regular research visits to Japan and collaboration with Doshisha University, Kyoto.

Applicants should have a PhD in mathematics or a related field and a strong record in convex analysis, optimization, or related areas.