Job Description

Full time: 25 Hours per week
Fixed term: 12 months
We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM orchestration, with applications to Finance.
This post is advertised as full-time (35 hours per week). We are open to considering requests for hybrid working (on a non-contractual basis) that combine a mix of remote and regular on-campus working.
The post is subject to Level 4 pre-employment screening.
The Opportunity:
Do you want to work at the forefront of AI research, tackling critical challenges around AI assurance, financial crime, synthetic data, LLM orchestration, and agentic workflows?
We are seeking a Postdoctoral Research Associate (PDRA) in Artificial Intelligence and Reinforcement Learning to join a collaborative initiative between the University of Edinburgh and the NatWest Group. This role offers a unique opportunity to contribute to high-impact research at the intersection of academia and industry, developing cutting-edge solutions for real-world challenges in financial services.
You will lead and contribute to innovative research projects in machine learning, synthetic data, LLM orchestration, and RL, helping shape the future of trustworthy and robust AI systems. The successful candidate will also engage with stakeholders, publish high-quality papers, and participate in dissemination activities including workshops and industry-facing events.
The candidate will be working under the supervision of Professor Lukasz Szpruch (University of Edinburgh and The Alan Turing Institute ) and Data Scientists at NatWest Banking.
Your skills and attributes for success:

• Expertise in one or more of the following areas: Machine Learning, Reinforcement Learning, or LLM orchestration.
• Strong programming skills in Python and relevant frameworks (e.g., PyTorch, JAX).
• Ability to scope, pilot, and deliver impactful research projects in collaboration with academic and industry partners.
• Track record of publishing high-quality research outputs and presenting findings through papers, workshops, and public engagement.