Postdoctoral Research Assistant

Corehr; Oxford; On-site; GBP 42,000 - 48,000; Full time

Job summary

University of Oxford's Department of Statistics seeks a Postdoctoral Research Assistant to develop the Genetic Inheritance Graph (GIG), integrating structural variants into genetic genealogies, and to contribute to novel computational methods.

You will work with Prof Ignatieva, Prof McVean, and Dr Wong, advance the giglib library, implement efficient data structures, and simulate genomes containing variants, with opportunities for collaboration across human, bacterial, and agricultural genetics.

Qualifications

• PhD/DPhil in statistics, mathematics, CS, or population genetics.
• Strong software development and algorithm design background.
• Motivation to work on genetics problems; prior genetics experience not required.
• Ability to manage own research and communicate findings.

Responsibilities

• Develop core giglib library components and data structures.
• Create algorithms to manipulate and analyse GIGs.
• Simulate genomes with structural variants and translate to pangenome graphs.
• Prepare publications and software outputs; present at meetings.

Skills

Coding experience

Software development

Algorithms

Population genetics

Education

PhD/DPhil in quantitative subject

Job description

UK date and time: 19-September-2026 18:07

Department of Statistics 24-29 St Giles', Oxford OX1 3LB

We are seeking to appoint a Postdoctoral Research Assistant to join the Department of Statistics at the University of Oxford, working on the development of the Genetic Inheritance Graph (GIG), a novel computational framework for integrating structural variants into genetic genealogies. This is an exciting opportunity to contribute to the development of new mathematical and computational methods that will enable researchers to capture genetic history more fully and investigate how structural variants influence human health, bacterial adaptation, and the productivity and resilience of crops and livestock.

You will work closely with the project team at Oxford, including Professor Anastasia Ignatieva, Professor Gil McVean, and Dr Yan Wong, with opportunities for wider collaboration with researchers at the University of Bath and leading international experts across human, bacterial, and agricultural genetics. You will contribute to the development of the core giglib software library, translating mathematical concepts underlying the GIG into efficient data structures and algorithms. Your work will also involve developing algorithms to manipulate and analyse GIGs, creating efficient methods for translating GIGs into pangenome variation graphs, and developing flexible and realistic simulations of genomes containing structural variants.

You will manage your own research activities, adapting existing methodologies and developing new computational approaches to address challenging problems in population genetics. You will analyse quantitative data, develop and refine computational methods, and contribute to high-quality scientific software and research outputs. You will also collaborate in the preparation of research publications and software outputs, participate in regular group meetings, and present your work at internal and external meetings, seminars, and conferences. The role offers an excellent opportunity to develop your research profile while working within a vibrant, world-leading community of statistical and population geneticists at Oxford.

It is essential that you hold, or are close to completing, a PhD/DPhil in a relevant quantitative subject such as statistics, mathematics, computer science, statistical or population genetics, or a related discipline. You should have relevant coding experience and a strong background in software development, algorithm design, and the production of high-quality scientific software. A strong motivation to work on problems in genetics is essential, although prior experience in genetics is not required. You should be able to manage your own research and associated activities, communicate effectively with a range of audiences, and contribute to research publications and presentations.

This position is offered full time on a fixed term contract for 3 years. Start date as soon as possible and no later than March 2027

The deadline for applications is midday 12 October 2026, and interviews are expected to take place on 27 and 28 October 2026.

For further details of the role please see the job description.