About the Project
Are you interested in contributing to supercharging the productivity of genome biologist researchers? We have an exciting 4-year PhD position open through the BBSRC LIDo Doctoral Training Programme to start in December 2018.
The project is highly interdisciplinary, including bioinformatic genome analysis, statistics, machine learning, programming and advanced data science visualisation. The aim of the project is to develop a means to electronically infer the potential functions of genes in organisms where no experimental data exists. This ability is crucial to our understanding of pathogens, pests, crops, and the natural world, and thus in turn for health and security.
The successful candidate will become part of the the London Interdisciplinary Biosciences Consortium which is one of the largest BBSRC funded Doctoral Training Partnerships in the UK. Representing an exciting collaboration between six of London’s world-class universities and specialist institutions, the consortium provides students with a unique opportunity to pursue innovative interdisciplinary research projects in the heart of one of the world’s most vibrant cities. Further information on the
The student will benefit from a well-rounded training programme to develop their expertise, as well as a strong network of colleagues and collaborators locally, and in other institutions in the UK and overseas. The student will learn best practices including in data science, scientific project management, scientific communication, and visualisation and analysis of genomic data. Applicants are therefore not expected to already be experts in the focal areas of research. Excellent candidates fulfill several of the following criteria: smart, curious, hard working, experienced with genomic data, not scared of data analysis or coding.
The PhD will be principally based at Queen Mary University of London (QMUL), but the student will have ample opportunity to travel for visiting collaborators, attending and presenting at conferences and workshops.
Potential candidates are encouraged to contact Dr Yannick Wurm ([Email Address Removed]) with informal enquiries regarding suitability, eligibility, scope or other aspects of the project.
Formal applications should be submitted directly to QMUL by the stated deadline using the ’Apply Online’ link on this page (please do not submit applications to the LIDo DTP).
Funding Notes
Applicants must meet our PhD entry requirements: https://www.qmul.ac.uk/sbcs/postgraduate/phd-programmes/entry-requirements/
The studentship is funded via the BBSRC LIDo DTP and will cover tuition fees and an annual tax-free maintenance allowance for 4 years at Research Councils UK rates (£16,777 in 2018/19). UK students, and EU students who have been ordinarily resident in the UK for at least 3 years are eligible for full BBSRC funding.
Applicants must be able to begin the PhD in December 2018.
References
[1] Sequenceserver: a modern graphical user interface for custom BLAST databases. Priyam et al. bioRχiv (2015) http://dx.doi.org/10.1101/033142
[2] An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Jian et al. Genome Biology (2016) https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1037-6
[3] Orthologous matrix (OMA) algorithm 2.0: more robust to asymmetric evolutionary rates and more scalable hierarchical orthologous group inference. Train et al. Bioinformatics (2017) https://doi.org/10.1093/bioinformatics/btx229
[4] GeneValidator: identify problems with protein-coding gene predictions. Dragan et al. Bioinformatics (2016) https://doi.org/10.1093/bioinformatics/btw015
[5] The genome of the fire ant Solenopsis invicta. Wurm et al. PNAS (2011) http://www.pnas.org/content/108/14/5679
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