Funded PhD OpportunityDevelopment of an integrated metagenomic analysis system using the microbial community, their genes and biological mechanisms to predict cattle phenotypes

Subject: Computer Science and Informatics


This is a 3.5 year studentship based at the School of Computing, Jordanstown, Ulster University, in collaboration with Scotland’s Rural College (SRUC).  It is envisaged that the student will spend 50% of their time with each of the collaborating bodies.  The project would suit a graduate with a Computer Science, Informatics and Bioinformatics background.

The overall aim of the project is to develop an integrated metagenomic analysis using the abundances of the rumen microbial community and microbial genes as well as their biological mechanisms to predict performance traits such as feed conversion efficiency, meat quality (Omega-3 fatty acids) and methane emissions in beef cattle to be used for genetic improvement programmes, nutritional interventions, precision farming, etc. to improve productivity, resource efficiency, economics, product quality with reduced environmental impact of animal production.

The PhD project is a continuation of our research published in PLOS genetics on the bovine microbiome (Roehe et al., 2016) and in Methods on metagenomics networks (Wang et al., 2017). An advanced multiplex network-based approach which is capable to handle the correlation between traits and incorporate diverse information multiple sources will be developed. The pipeline will be provided as open source software so that its use will be widespread and improvement and adaption to specific analysis can be achieved easily after the end of the research project.

The studentship will be based at School of Computing at Jordanstown Campus, Ulster University, and will be jointly supervised by Prof. Huiru(Jane) Zheng (Ulster), Prof. Rainer Roehe (SRUC), Dr. Haiying Wang (Ulster) and Prof Richard Dewhurst(SRUC). The student will be trained at Ulster University (50% of time) in machine learning, bioinformatics, metagenomic analysis, etc.  and at SRUC  (50% of time) in currently used metagenomic analysis, microbiology, animal genetics and nutrition, etc.

This project would suit someone with an interest in bioinformatics, metagenomics, big data analytics and computational biology. Applicants should have or expect to obtain a minimum of an upper second class honours degree (or equivalent) in a relevant computer science or informatics subject area. Excellent numeracy and communication skills (both verbal and written) are required.

Applicants should hold ordinary UK/EU residence to be eligible for both fees and maintenance. All applicants should hold a first or upper second class honours degree in Computer Science or a related discipline. Successful candidates will enroll on a full-time research programme, of up to 3.5 years subject to satisfactory progress, leading to the award of the degree of Doctor of Philosophy.

Essential Criteria

  • Upper Second Class Honours (2:1) Degree from a UK institution (or overseas award deemed equivalent via UK NARIC)

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%

Other Funders

Scotland's Rural College / Faculty of Computing, Engineering and the Built Environment, Ulster University: The scholarship will cover tuition fees and maintenance award of not less than £14,553 per annum for three years (subject to satisfactory academic performance). Applications are invited from UK and EU candidates only.

Other information

Launch of the Doctoral College

Current PhD researchers and an alumnus shared their experiences, career development and the social impact of their work at the launch of the Doctoral College at Ulster University.

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Key Dates

Submission Deadline
Friday 2 March 2018
Interview Date
12 March 2018 - 23 March 2018

Contact Supervisor

Professor Jane Zheng

Other Supervisors

Apply online

Visit and quote reference number #243262 when applying for this PhD opportunity