At a Glance
- Tasks: Join a team advancing machine learning for high-stakes areas like law and finance.
- Company: Be part of Royal Holloway, University of London, a leader in computer science research.
- Benefits: Enjoy competitive salary, generous leave, training programmes, and hybrid working options.
- Other info: Work in a vibrant environment with excellent career growth opportunities.
- Why this job: Make a real impact in trustworthy AI and contribute to vital decision-making processes.
- Qualifications: Master's degree or nearing PhD in relevant fields; strong programming and communication skills required.
The predicted salary is between 34670 - 40284 £ per year.
Applications are invited for the post of Research Assistant in Applied Machine Learning for High-Stakes Regulated Domains in the Department of Computer Science at Royal Holloway, University of London. This is a full-time, fixed-term post for one year from September 2026, with some flexibility around the start date.
The project seeks to advance the development and application of reliable, interpretable and uncertainty-aware machine learning methods for high-stakes regulated domains, including law, finance, policy and regulatory decision-making. These are settings in which errors may have significant legal, financial or social consequences, and where machine learning systems must be robust, transparent and trustworthy.
The post holder will contribute to research on applied machine learning methods that support robust decision-making under uncertainty. This may involve working with structured datasets, panel data, text data, regulatory documents, legal materials, financial information and policy reports. The role will include designing and evaluating machine learning models, developing experimental studies, analysing research findings, and contributing to academic publications and wider dissemination.
The successful candidate will have a Master's degree, or be near completion of a PhD in a discipline relevant to high-stakes regulated domains, such as regulation, finance, public policy, governance, or socio-legal studies. They should have a strong interest in applying machine learning and data-driven methods to legal, financial, policy or regulatory decision-making. They will have strong knowledge of machine learning research methods, excellent programming skills, and the ability to communicate complex ideas clearly to academic and non-specialist audiences. Experience in trustworthy AI, uncertainty quantification, interpretability, robustness, high-stakes decision-making, law, finance or regulation would be highly desirable.
The role offers an excellent opportunity to contribute to an emerging and important area of applied machine learning research, with potential impact across sectors where AI systems must be reliable, accountable and safe. The post holder will be expected to conduct both independent and collaborative research, develop and evaluate machine learning methods, contribute to publications in leading academic venues, identify new research directions, and support the wider goals of the project.
The successful candidate will join a vibrant and supportive research environment within the Department of Computer Science at Royal Holloway, University of London, and will contribute to the growing area of trustworthy AI and machine learning for high-stakes regulated domains.
In return, we offer a highly competitive rewards and benefits package, including:
- Generous annual leave entitlement
- Rich training and development programmes
- A pension scheme with generous employer contribution
- Various schemes, including Cycle to Work, Season Ticket Loans and help with the cost of eyesight testing
- Free parking
The post is based in Egham, Surrey, where the University is situated on a beautiful, leafy campus near Windsor Great Park and within commuting distance of London.
For an informal discussion about the post, please contact Dr Khuong An Nguyen at Khuong.Nguyen@rhul.ac.uk. For queries on the application process the Human Resources Department can be contacted by email at: recruitment@rhul.ac.uk.
Please quote the reference: 0826-269. Closing Date: 23:59, 7 August 2026. Interview Date: To be confirmed.
Research Assistant in Applied Machine Learning for High-Stakes Regulated Domains in Egham employer: Allscreens Nationwide Ltd
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