Mario dos Reis
11 months ago
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Developing Efficient Bayesian Samplers for Evolutionary Analysis in the Tree of Life Queen Mary University of London in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Funded PhD Project (Students Worldwide)
Deadline
Expired
Country
United Kingdom
University
Queen Mary University of London
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Keywords
About this position
This PhD project at Queen Mary University of London, supervised by Dr Mario dos Reis and Prof Richard A Nichols, focuses on developing efficient Bayesian samplers for evolutionary analysis across the Tree of Life. The research leverages high-throughput sequencing technologies, which generate vast genomic datasets from sources such as tumour cell collections and millions of species. These datasets are crucial for understanding molecular evolution, reconstructing evolutionary relationships, and calibrating evolutionary trees to geological time. Bayesian statistical methods, particularly MCMC samplers like Metropolis-Hasting and Hamiltonian Monte Carlo, are state-of-the-art for these analyses but are computationally intensive.
The student will design and implement new computational approaches to improve the efficiency of these samplers, with a special emphasis on methods for determining diversification timings. Applications of these methodologies will include case studies on cancer cells, mammals, birds, and flowering plants, addressing fundamental questions about evolutionary patterns. The dos Reis and Nichols labs are renowned for their work in statistical methodologies for evolutionary studies, and the School of Biological and Behavioural Sciences offers a vibrant research environment with active groups in genomics and molecular evolution.
The project is ideal for candidates with backgrounds in computational biology, genomics, statistical modelling, Hamiltonian dynamics, or computer science. Funding is available through a partnership with the China Scholarship Council (CSC), covering tuition fees, living expenses, and travel for eligible Chinese applicants. Applicants must be citizens and permanent residents of China, hold a Chinese passport, and meet both CSC and QMUL eligibility criteria, including English language requirements. The application deadline is January 28, 2026, and candidates must submit all required documentation, including evidence of English proficiency, ahead of the CSC deadline.
Shortlisted candidates will be interviewed, and successful applicants will receive a conditional offer from QMUL before applying to CSC for the scholarship. For further details, applicants are encouraged to consult the dos Reis lab and Nichols group websites, as well as the QMUL China Scholarship Council webpage.
Funding details
Funded PhD Project (Students Worldwide)
What's required
Applicants must be citizens and permanent residents of the People’s Republic of China, hold a Chinese passport, and be applying for CSC funding. Candidates should have or expect to receive a first or upper-second class honours degree and a Master’s degree in a relevant area such as Evolutionary Biology, Computational Biology, Statistical Analysis, or related fields. Knowledge of programming in a major language (such as R or C) and large data analysis is highly advantageous but not required. Experience in evolutionary biology/phylogenetic analysis or Bayesian statistical techniques is preferred. Applicants must meet the IELTS requirements for the course and upload evidence before the CSC application deadline, ideally by March 1, 2026.
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Professors
Dr at School of Biological and Behavioural Sciences
United Kingdom
Research Interests
Paleontology
Zoology
Evolutionary Biology
Molecular Evolution
Molecular Phylogeny
Prof at School of Biological and Behavioural Sciences
United Kingdom
Research Interests
Statistic
Cancer Genetics
Evolutionary Biology
Comparative Biology
High-throughput Sequencing
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