FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW FREE Virtual Study Fair | 1 - 2 March | REGISTER NOW

China Scholarship Council- Polymorphism-aware phylogenies and machine learning


   School of Biology

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr C Kosiol  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

BACKGROUND

Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Alternative models accounting for multi-individual data have nevertheless been proposed and are known as polymorphism-aware phylogenetic models (PoMo, De Maio et al., 2015). PoMo adds a new layer of complexity to the standard nucleotide substitution models by accounting for the population-level (so far, genetic drift and mutations) processes to describe the evolutionary process. To do so, PoMo expands the standard substitution models to include polymorphic states. We have previously shown that PoMo accounts for incomplete lineage sorting (ILS) and improve the estimation of species tree inference.

Recently we have developed an approach that accounts for selection called PoMoSelect (Borges et al., 2022). Genetic drift removes polymorphism from populations over time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. PoMo is a mutation-selection model which we will allow disentangle selection as well as mutational effects, fixation biases, and demographic effects.

As the complexity of our PoMo models grows we hope to use machine learning techniques that have recently been developed to allow the estimation of parameters from large-scale data using deep learning (Voznica et al., 2022). This will allow us to study evolutionary processes across the tree of life with our collaborators. 

Informal enquiries are welcome and should be made by email to Carolin Kosiol ([Email Address Removed])

HOW TO APPLY

Details of the competition and how to apply are given here: https://www.st-andrews.ac.uk/study-abroad/global-research/csc/

Applicants must submit an application for entry onto the School's PhD programme. Please do this via the Online Application Portal by the 9th January 2023. Once submitted, applicants must also submit a separate application for the scholarship by Thursday 12th January 2023.

We require the following documents; CV, personal statement, 2 references, academic qualifications, English language qualification.

Keywords: Speciation, phylogenetics, molecular evolution, comparative genomics, machine learning, bioinformatics


Funding Notes

This position is funded by the Chinese Scholarship Council (CSC) for a duration of 4 years with a start date of Sept 2023. Qualified candidates will be supported in applications for appropriate funding sources, including, for example, scholarships from learned societies or CSC studentships. Self-funded applicants can be considered as well.

References

Borges R, Boussau B, Szöllősi GJ, Kosiol C (2022). Nucleotide usage biases distort inferences of the species tree. Genome Biology and Evolution 14 (1): evab290.
De Maio N, Schrempf D, and Kosiol C (2015). PoMo: An allele frequency-based approach for species tree estimation. Systematic Biology, 64(6):1018.
Voznica J, Zhukova A, Boskova V et al. (2022). Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks. Nat Commun 13: 3896.

How good is research at University of St Andrews in Biological Sciences?


Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs