Career Opportunities at Queen's

About the job:

This Postdoctoral Research Fellow position will lead the spatial modelling and habitat association components of the MOSAIC (Multi-disciplinary Ocean Sensing for Adaptive International Conservation) project, funded through PEACEPLUS and managed by the Special EU Programmes Body. Based in the School of Biological Sciences at Queen’s University Belfast, the role sits within a dynamic marine ecology and conservation research group working at the interface of quantitative science and policy impact.

The successful candidate will design and implement advanced geospatial and statistical workflows to analyse AUV and BRUV datasets collected along the Northern Ireland–Donegal Bay coastline. Using machine-learning and multivariate modelling approaches, they will quantify habitat associations of OSPAR-listed elasmobranchs and generate high-resolution predictive distribution maps. Outputs will directly inform cross-border marine spatial planning and conservation management, including the identification of critical habitats such as flapper skate egg-laying areas.

The postholder will work closely with the Principal Investigator, project partners including the Loughs Agency, and interdisciplinary collaborators across ecology, oceanography and marine technology. In addition to producing policy-relevant geospatial products, the Fellow will contribute to peer-reviewed publications, technical reporting, and postgraduate supervision.

This is an opportunity to apply cutting-edge quantitative ecology within a high-impact conservation programme, contributing to evidence-based management of vulnerable marine species in a transboundary context.

About the person:

We are seeking to engage a highly capable Postdoctoral Research Fellow with strong quantitative, geospatial, and analytical expertise to lead the spatial modelling activities of the MOSAIC Project. The ideal candidate will be an organised, independent, and collaborative researcher who can integrate advanced ecological understanding with rigorous data analysis.

Applicants must:

• Hold a PhD (or equivalent) in Marine Science or a related quantitative discipline with a focus on elasmobranchs.
• Demonstrate experience in marine spatial and multivariate modelling of ecological or environmental datasets and proficiency in coding environments such as R / Python / Matlab; track record of peer reviewed publications.
• Have experience analysing AUV, BRUV or other marine video datasets;
• Demonstrate advanced GIS capability (ArcGIS Pro or QGIS) is essential, as is confidence in applying machine-learning approaches such as GAMs, Random Forest, or boosted regression models.
• Have understanding of benthic habitat mapping and spatial data standards (EMODnet, INSPIRE)
• Familiarity with OSPAR species ecology or JNCC/EUNIS habitat classification systems.
• Strong communication skills are required, including the ability to present spatial analyses effectively through maps, reports, and publications to scientific, stakeholder, and policy audiences.
• The successful candidate will be able to work productively within a cross-border, multi-institutional team, engaging closely with the Loughs Agency, project partners, and postgraduate researchers.
• We are looking for someone analytical, self-motivated, and able to manage complex datasets while meeting project timelines. A willingness to travel to coastal field sites and partner meetings across Northern Ireland and Donegal Bay is essential.
• This role suits a researcher committed to producing high-impact geospatial science that supports marine conservation and policy development.
• Willingness to travel to coastal field sites and partner meetings is required.

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information.

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.