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School of Biological and Behavioural Sciences

Efficient sampling of evolutionary trees with thousands of species: from viral phylogenies to the tree of life

Research environment

The School of Biological and Behavioural Sciences at Queen Mary is one of the UK’s elite research centres, according to the 2021 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 180 PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.

Training and development

Our PhD students become part of Queen Mary’s Doctoral College which provides training and development opportunities, advice on funding, and financial support for research. Our students also have access to a Researcher Development Programme designed to help recognise and develop key skills and attributes needed to effectively manage research, and to prepare and plan for the next stages of their career.

Project description

High-throughput sequencing technologies are generating vast amounts of molecular sequence data. In the case of viruses, such as SARS-Cov2, millions of viral isolates have been sequenced. Efficient computational technologies are required to extract value and information from these vast datasets. For example, by building large-scale evolutionary trees of viruses, it is possible to estimate timings and patterns of branching, evolutionary rates, and epidemiological parameters.

In this project, the student will help in the development, implementation and testing of a Hamiltonian Monte Carlo (HMC) sampler for the Bayesian analysis of viral evolutionary trees. A prototype HMC sampler has already been developed in the lab, and has shown exceptional computational performance improvements.

The project is suited for a student with an interest in big data analysis, Bayesian data analysis, computer programming and evolutionary biology. The methodology developed here can be easily extended to other challenges, such as reconstructing the tree of life from genomic data, including that being generated by large scale sequencing consortia such as the Darwin Tree of Life Project.

We would not expect the successful candidate to have existing expertise across all the disciplines required for the project, and would provide tailored training as appropriate. The most important quality is an appetite for understanding rules and processes governing the evolution of life and some proven ability in one of the above disciplines.

Funding

This studentship is open to students applying for China Scholarship Council funding. Queen Mary University of London has partnered with the China Scholarship Council (CSC) to offer a joint scholarship programme to enable Chinese students to study for a PhD programme at Queen Mary. Under the scheme, Queen Mary will provide scholarships to cover all tuition fees, whilst the CSC will provide living expenses for 4 years and one return flight ticket to successful applicants.

Eligibility and applying

Applicants must be:
- Chinese students with a strong academic background.
- Students holding a PR Chinese passport.
- Either be resident in China at the time of application or studying overseas.
- Students with prior experience of studying overseas (including in the UK) are eligible to apply. Chinese QMUL graduates/Masters’ students are therefore eligible for the scheme.

Please refer to the CSC website for full details on eligibility and conditions on the scholarship. 

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree in an area relevant to the project. A masters degree is desirable, but not essential.
The most important quality for successful candidates is an appetite for understanding rules and processes governing the evolution of life. You should have some proven ability in at least one of the following disciplines: Bayesian data analysis, computer programming and / or evolutionary biology. We will provide tailored training for the candidate in the areas where they have less experience.

Applicants from outside of the UK are required to provide evidence of their English Language ability. Please see our English Language requirements page for details: https://www.qmul.ac.uk/international-students/englishlanguagerequirements/postgraduateresearch/   

Informal enquiries about the project can be sent to Professor Richard Nichols at r.a.nichols@qmul.ac.uk 

Formal applications must be submitted through our online form by 31st January 2024 for consideration, including a CV, personal statement and qualifications. You must meet the IELTS/ English Language requirements for your course and submit all required documentation (including evidence of English Language) by 14th March 2024. You are therefore strongly advised to sit an approved English Language test as soon as possible. 

Shortlisted applicants will be invited for a formal interview by the supervisor. If you are successful in your application, then you will be issued an QMUL Offer Letter, conditional on securing a CSC scholarship along with academic conditions still required to meet our entry requirements. Once applicants have obtained their QMUL Offer Letter, they should then apply to CSC for the scholarship by in March 2024 with the support of the supervisor.

Only applicants who are successful in their application to CSC can be issued an unconditional offer and enrol on our PhD programme. For further information, please go to: https://www.qmul.ac.uk/scholarships/items/china-scholarship-council-scholarships.html 

Apply Online

References

Álvarez-Carretero, S., Tamuri, A. U., Battini, M., Nascimento, F. F., Carlisle, E., Asher, R. J., ... & Dos Reis, M. (2022). A species-level timeline of mammal evolution integrating phylogenomic data. Nature, 602(7896), 263-267.
Stevens, Michael CA, Sally C. Faulkner, André BB Wilke, John C. Beier, Chalmers Vasquez, William D. Petrie, Hannah Fry, Richard A. Nichols, Robert Verity, and Steven C. Le Comber. "Spatially clustered count data provide more efficient search strategies in invasion biology and disease control." Ecological applications 31, no. 5 (2021): e02329.
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