Research Fellow - Statistical Safeguarding x2 (111576-0426)
CompanyUniversity of Warwick
Location
Coventry, England, United Kingdom
Employment TypeContract
Posted At5/3/2026
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Occupation TypeBusiness and related research professionals
Occupation Code Skill LevelHigher Skilled
Sponsorship Salary Threshold
£41,700
(£21.38 per hour)
Standard minimum applies
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Description
About The Role
We invite applications for two 2-year Research Fellows to join the Statistical Safeguarding programme, based at the University of Warwick.
The vision of the programme is to develop world-leading fundamental research in safe and interpretable AI. The programme aims to investigate learning systems that maintain robustness in real-work environments when the data quality is compromised. The program focuses on mathematical understanding of data corruption, theoretical modelling of its impact on learning and the development of mitigation strategies.
This £2M programme is funded by EPSRC and brings together research groups from the Universities of Edinburgh, Oxford and Warwick in the UK, together with Universities of Tokyo and Osaka in Japan. Interactions between the research groups at the universities will be strongly encouraged and resourced, with broad research projects to involve substantial cross-institutional collaborations. This UK-Japan collaborative consortium will provide opportunities for international exposures on workshops, summer schools and supervisory activities.
Candidates who are considering making an application are strongly encouraged to discuss the programme in greater detail by contacting:
You should have, or be close to completing, a PhD in Mathematics, Statistics, Machine Learning or a related discipline.
The grant’s research programme involves developing mathematical understanding of AI that will lead to more reliable, interpretable and uncertainty-aware AI systems. We are keen to encourage applications from anyone with the mathematical skills needed to undertake such research. Experience working in AI, whilst desirable, is not required.
Relevant mathematical background includes, but is not limited to: statistical estimation theory, optimal transport, inverse problems, numerical analysis, probability, statistical machine learning, stochastic analysis and uncertainty quantification.
See Statistical Safeguarding | github for mathematical skills required for the position.
You will have demonstrated the ability to develop either new methodology or new mathematical understanding. We are particularly keen to encourage applicants with strong theoretical skills and are looking to put together a team of researchers with broad skills that cover relevant areas of mathematics, statistics, machine learning and AI.
A demonstrable ability to produce academic writing of the highest publishable quality is essential.
For details on the experience and skills required, please refer to the job description attached as a PDF below.
We invite applications for two 2-year Research Fellows to join the Statistical Safeguarding programme, based at the University of Warwick.
The vision of the programme is to develop world-leading fundamental research in safe and interpretable AI. The programme aims to investigate learning systems that maintain robustness in real-work environments when the data quality is compromised. The program focuses on mathematical understanding of data corruption, theoretical modelling of its impact on learning and the development of mitigation strategies.
This £2M programme is funded by EPSRC and brings together research groups from the Universities of Edinburgh, Oxford and Warwick in the UK, together with Universities of Tokyo and Osaka in Japan. Interactions between the research groups at the universities will be strongly encouraged and resourced, with broad research projects to involve substantial cross-institutional collaborations. This UK-Japan collaborative consortium will provide opportunities for international exposures on workshops, summer schools and supervisory activities.
Candidates who are considering making an application are strongly encouraged to discuss the programme in greater detail by contacting:
- Dr Wenkai Xu at Wenkai.Xu@warwick.ac.uk.
- Professor Yi Yu at Yi.Yu.2@warwick.ac.uk.
- Professor Giovanni Montana at G.Montana@warwick.ac.uk.
- Dr Matthew Thorpe at Matthew.Thorpe@warwick.ac.uk.
You should have, or be close to completing, a PhD in Mathematics, Statistics, Machine Learning or a related discipline.
The grant’s research programme involves developing mathematical understanding of AI that will lead to more reliable, interpretable and uncertainty-aware AI systems. We are keen to encourage applications from anyone with the mathematical skills needed to undertake such research. Experience working in AI, whilst desirable, is not required.
Relevant mathematical background includes, but is not limited to: statistical estimation theory, optimal transport, inverse problems, numerical analysis, probability, statistical machine learning, stochastic analysis and uncertainty quantification.
See Statistical Safeguarding | github for mathematical skills required for the position.
You will have demonstrated the ability to develop either new methodology or new mathematical understanding. We are particularly keen to encourage applicants with strong theoretical skills and are looking to put together a team of researchers with broad skills that cover relevant areas of mathematics, statistics, machine learning and AI.
A demonstrable ability to produce academic writing of the highest publishable quality is essential.
For details on the experience and skills required, please refer to the job description attached as a PDF below.
PhD Status
The Department of Statistics is committed to promoting equality and diversity, holding an Athena SWAN Silver award which demonstrates this commitment.
We welcome applicants from all sections of the community and will give due consideration to applicants seeking flexible working patterns, and to those who have taken a career break.
Further information about working at the University of Warwick, including information about childcare provision, career development and relocation is at https://warwick.ac.uk/services/humanresources/workinghere/
About The University
We are a world-leading research-intensive university founded in 1965. We are ranked 74th in the world and 9th in the UK.* Additionally, 92% of our research is rated world-leading or internationally excellent.**
Find out more about us at warwick.ac.uk/about/.
CLOSING DEADLINE: Wednesday 27th May 2026 at 11:55pm (UK Time)
To apply, please click APPLY below and submit your application by the closing deadline.
You will be asked to include a CV and cover letter. Within these documents, please:
- If you are near submission of your PhD, or have not yet had it conferred, any offers of employment will be made at Research Assistant level, at the highest spinal point of pay grade 5 (£34,610 per annum).
- Upon receipt of evidence confirming the successful award of your PhD, you will be promoted to Research Fellow, at the lowest spinal point of grade 6 (£35,608 per annum).
The Department of Statistics is committed to promoting equality and diversity, holding an Athena SWAN Silver award which demonstrates this commitment.
We welcome applicants from all sections of the community and will give due consideration to applicants seeking flexible working patterns, and to those who have taken a career break.
Further information about working at the University of Warwick, including information about childcare provision, career development and relocation is at https://warwick.ac.uk/services/humanresources/workinghere/
About The University
We are a world-leading research-intensive university founded in 1965. We are ranked 74th in the world and 9th in the UK.* Additionally, 92% of our research is rated world-leading or internationally excellent.**
Find out more about us at warwick.ac.uk/about/.
- World University Ranking 2026, Complete University Guide 2026
- Research Excellence Framework 2021
CLOSING DEADLINE: Wednesday 27th May 2026 at 11:55pm (UK Time)
To apply, please click APPLY below and submit your application by the closing deadline.
You will be asked to include a CV and cover letter. Within these documents, please:
- Outline your employment and education history (including your most recent employment).
- Outline the reasons for your interest in this position.
You are welcome to include any careers breaks you may have taken (e.g. parental/caring/long-term sick leave).
Please note that if you do not evidence the essential criteria, the hiring panel may not be able to shortlist your application. For guidance on how to format a cover letter, see here.
What we Offer
We Provide a Comprehensive Range Of Benefits, Including
- An attractive pension scheme.
- 30 days holiday plus University Christmas closure.
- Generous parental/adoption leave policy.
- Onsite childcare facilities.
- Excellent learning and development opportunities.
We are proud to be a Living Wage employer.
Our Commitment to Inclusion
Equality, Diversity, & Inclusion
Warwick is committed to fostering a diverse, inclusive and respectful community where everyone can thrive. We welcome applications from all backgrounds, cultures, and communities, and actively encourage candidates from underrepresented groups to apply.
Find out more about our Social Inclusion work at Warwick.
Find out more about our awards and accreditations.
We are also one of the six founder institutions of the EUTOPIA European University Alliance.
Safeguarding & DBS
The University of Warwick is committed to safeguarding and promoting the welfare of all those we work with.
- Roles involving regulated activity are subject to a Disclosure and Barring Service (DBS) check at the appropriate level, in line with the DBS Code of Practice.
The University will not discriminate against applicants who make a disclosure relating to a conviction.
Disclosures at application stage are only visible to the DBS team, not to hiring panels. This ensures fairness and removes bias from the shortlisting process.
Job Description
JD - Research Fellow - Statistical Safeguarding x2 (111576, 111577).pdf – 107KB Opens in a new window
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