Two graduate position opportunites at the dos Reis lab at Queen Mary
University of London to work on Bayesian phylogenetics.

Start: October 2025.
Application deadlines for both projects: January 2025.

The first project is sponsored by NERC (UK) and is part of the new London
Doctoral Landscape awards:

INTEGRATING MORPHOLOGY AND GENOMES TO INFER EVOLUTIONARY TIMESCALES

Large biological datasets are accumulating at a fast pace, such as
genome sequences from the EarthBiogenome project or 3D scan data from
museum specimens. These datasets can be integrated to resolve evolutionary
timelines and relationships among species, in turn allowing the testing of
precise hypothesis about patterns of species diversification through time
and their relationship to the past climatic and geological history of the
planet, including extinction events. In this project, the student will
work in the development and application of Bayesian MCMC methodologies
for analysis of genomic and morphological data to resolve evolutionary
timescales. The new methodologies will be applied to case studies on the
diversification of plant and animals, allowing a deeper understanding
of how biodiversity on Earth arose, and inform studies of the potential
impacts of climate change on future biodiversity.

Deadline: 20th January 2025.

Further info and application procedure:
https://www.trees-dla.ac.uk/projects/integrating-morphology-and-genomes-infer-evolutionary-timescales

The second project is sponsored by the China Scholarship Council.

DEVELOPING EFFICIENT BAYESIAN SAMPLERS FOR EVOLUTIONARY ANALYSIS IN THE
TREE OF LIFE

Application of high-throughput sequencing technologies is leading to
the generation of vast genomic datasets, from to the collection of cells
in a tumour to the millions of species in the Tree of Life. These large
datasets can be analysed to understand patterns of molecular evolution,
to work out the evolutionary relationship between species/cells, and to
calibrate evolutionary trees to geological time. Bayesian statistical
methods have become the state-of-the-art for such analyses but are
computationally expensive. In this project, the student will develop
new computational implementations of Bayesian MCMC samplers (such as
Metropolis-Hasting and Hamiltonian Monte Carlo) for evolutionary studies,
with emphasis on methods to determine diversification timings. The new
methodologies can be applied to understand diversification patterns in
case studies such as cancer cells, mammals, birds, and flowering plants,
among others, helping to answer fundamental questions about evolutionary
patterns. The project is suited for a student with background in any
relevant areas such as computational biology, genomics, statistical
modelling, Hamiltonian dynamics, and computer science.

Deadline: 25th January 2025 (this is the QMUL deadline, successful
applicants can then apply to CSC around March).

Further info and application procedure:
https://www.findaphd.com/phds/project/developing-efficient-bayesian-samplers-for-evolutionary-analysis-in-the-tree-of-life/?p176899

Mario dos Reis

Reader in Statistical Phylogenetics
Queen Mary University of London
https://dosreislab.github.io

Mario Dos Reis Barros <m.dosreisbarros@qmul.ac.uk>

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