Massively Parallel Phylogeny Reconstruction for the Age of DNA Big Data
Supervisors:
Dr Daniel Barker, University of Edinburgh, UK, [email protected]
and
Professor Thomas Meagher, University of St Andrews, UK
Note:
This opportunity is only open to UK nationals (or EU students who have been resident in the UK for 3+ years immediately prior to the programme start date) due to restrictions imposed by the funding body.
Project Description:
How are species related? This question, of fundamental importance across life sciences, can in principle be addressed using DNA sequence data. Implicit in these data is the pattern of relationships of the species the DNA came from, known as their phylogeny, usually represented as a tree. However, algorithms and software to reconstruct phylogenetic trees for very large input are lagging behind the recent explosion in availability of DNA sequence data.
The goal of this project is to create parallel algorithms and open-source software for reconstructing large phylogenies by heuristic searches suited to very large data.
The project will use theoretical and computational approaches. These will include use and characterisation of nature-inspired advanced heuristics (e.g. Strobl and Barker 2016), programming and use of massively parallel computer systems, optimisation of algorithms and implementations, and cross-site, distributed machine learning techniques.
Research training will be provided through various means, including meetings with the supervisors and with Dr Martyn Winn (STFC Scientific Computing Department), attendance at short courses and conferences, and attendance at local and regional seminars and discussion groups.
It is anticipated the successful candidate would gain valuable skills and insight for subsequent employment in computational science or life sciences, in academia or industry.
Literature Reference:
Strobl MAR, Barker D (2016) On simulated annealing phase transitions in phylogeny reconstruction. Molecular Phylogenetics and Evolution, 101, 46-55.
Further Information:
Before applying, potential applicants are encouraged to contact Daniel Barker informally.
Application:
Deadline 13 March 2018.
Please follow the instructions on how to apply at
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