Data Analyst/R Programmer in Fisheries Queen’s University Belfast United Kingdom

Queen’s University Belfast

Data Analyst/R Programmer in Fisheries

Science Computation                                           

Ref: 18/106932

School of Biological Sciences


The School of Biological Sciences aims to enhance the way we use technology in research, food security, microbiology, ecosystem biology, sustainability and many other related areas.  The School prides itself on providing innovative teaching methods and a high-tech learning environment for our students. 


This temporary post is available until 31 October 2019.


We seek an experienced scientific computation / data analyst to help build modelling tools for fisheries management as part of a European-wide consortium project, funded by the European Commission. The project “Protecting by-caught species in mixed fisheries (PROBYFISH)” aims to asses and develop methods of protecting inadvertently captured species in European mixed fisheries, improving the Common Fisheries Policy of the EU.  Many of by-catch species are ‘data-poor’, have no formal stock-management and some are endangered.

Our methods involve the use of management strategy evaluation (MSE) applied to case studies in different EU fisheries. Each case study is developing a mixed fishery management model (mainly using R-based tools: e.g. FLBEIA with FLR).

The role of the post-holder is to support the fisheries scientists who are building these tools by deploying advanced programming and data analysis knowledge, writing code for, and advising, them.


They will assist in implementing the MSE, including management of versions and code.

This will involve working closely with model-building scientists in various EU countries as well as developing modelling code (in R and related scripting languages), including for high-performance computer implementation, based in Queen’s University Belfast (QUB), School of Biological Sciences.


Based at QUB, the post-holder will be part of a tight team of fisheries science modellers working on several projects concerning ‘Ecosystem Based Fisheries Management’, in the context of a vibrant research culture with associated marine laboratory facilities, a computational biology suite and a community of professional scientists and post-doctoral research students, also maintaining close links with the Marine Institute in Galway, Ireland and ICES.

The post will likely involve some travel to Fisheries institutes in Denmark, Spain and Holland as well as Ireland.


The successful candidate will:


- Hold a 2.1 (or above) University Honours Degree (or equivalent) in a subject with demonstrated significant programming and/or scientific computing components.

- Have a minimum of 3 years’ experience in scientific programming and data analysis, including development of numerical computation algorithms, coding and all stages of software development including experience in programming R scripts to a professional technical standard (i.e. not just using R for statistics). 

- In addition it is desirable that the candidate will have previous experience in modelling for fisheries science, especially using R-based tools such as FLR and FLBEIA or F-cube. Ideally they will have knowledge of methods of stock assessment and approaches for data-poor stock.


Further information about the School can be found at


Further information from Dr Audric Vigier


Anticipated interview date: Tuesday 18 December 2018


Salary scale:   £33,199 to £36,261 per annum


Closing date: Wednesday 5 December 2018


For full criteria and to apply online, visit You must clearly demonstrate how you are meeting the criteria on your application. 


For further information or assistance contact Resourcing Team, Queen’s University Belfast, BT7 1NN. 


Telephone (028) 9097 3044 or email on


The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability. 


Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

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