Computational Research Fellow

The Co-Centre for Sustainable Food Systems (www.foodcocentre.org) brings together academics from multiple disciplines, including the natural sciences, trade, economics, politics, political economy and law. It will build on the shared objectives to develop a robust, resilient and sustainable food system that meets changing consumer demands. It will also look to foster cooperation and consensus to develop an agri-food sector that underpin national economies and will foster R&I to accelerate radical transitions towards a more environmentally and economically sustainable and transparent agri-food sector. The Co-Centre presents a unique opportunity to rapidly develop innovative and transformative solutions to transition the food system and position Ireland and the UK as a research and innovation global leader for positive and sustainable change in the transition to climate-neutrality by 2050.

We are seeking an enthusiastic individual to work within the Data Modelling Platform of the Co-Centre. This computational research role within the Institute for Global Food Security at Queen’s University Belfast will focus on consolidation and integration of food systems data from across the Co-Centre, developing and implementing computational solutions to promote Findable, Accessible, Interoperable, and Reusable Data practices and working in conjunction with researchers from Momentum One Zero (momentumonezero.com) to explore approaches for best use of AI across the Co-Centre.

This role will involve engagement with researchers across the Co-Centre to promote best practice in data collection and re-use and to generate data catalogues to facilitate future modelling activities.

About the person:

We are looking for someone with or about to obtain a PhD degree in Computer Science, Computational Biology or other relevant discipline with relevant practical experience handling and analysing large datasets. The candidate should have experience in approaches for generating findable, Accessible, Interoperable and Reusable (FAIR) data and a track record of senior author publications commensurate with stage of career.

Candidates with experience working with biological datasets, especially as related to Food Systems and/or knowledge of state-of-the-art database approaches that support FAIR principles are especially encouraged to apply. We would also welcome candidates with knowledge of AI approaches for understanding diverse biological datasets.

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 document on our website.

Advert information