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Using data analytics to identify best practices across the food supply chain that drive nutrition quality

Project Description

Project details:

Approaching nutrition quality from an analytics perspective stands to help us understand and predict the impact of food production practices on human and planetary health. This project is designed to make maximal use of existing data generated from studies conducted across various Schools (Biological Sciences; Medicine, Dentistry, and Biomedical Sciences; Mathematics) and with external collaborators e.g. AFBI, AFQCC. Much attention is given to the need to transform food production practices to ensure future food security. Nutrition quality must be central to these solutions, increasing yield is not enough. This project will ensure we first understand the impact existing practices have on nutrition quality and indicators of environmental impact e.g. the impact of different growing/feeding practices on micronutrient content, cultivar/seasonal differences and how, if modelled in accordance with existing consumption practices, these could impact health. Outputs will inform food production practices, ultimately delivering products of high nutritional quality with minimal environmental impact. By employing data analytic techniques this project will ensure we make the best use of existing research by connecting data from across the food supply chain and exploring it from nutrition, human and planetary health perspectives. This will ensure we understand existing best practice from a holistic perspective and will also inform future practice/innovations.

This project will be supervised by Dr Danielle McCarthy and Dr Anne Nugent (Queen’s University School of Biological Sciences/Institute for Global Food Security) and Dr Jose Sousa (Queen’ s University School of Medicine, Dentistry and Biomedical Sciences).

All applications MUST be submitted through

Specific skills/experience required by applicants:

A background of computer science with a personal interest in nutrition and/or food production. Ideally experience in integrating data driven insights for a commercial sector or public body.

All applicants must meet the academic entry requirements:

Funding Notes

Only UK and EU students are eligible to apply. Before applying, it is strongly recommended that you read the full information on eligibility criteria available from DfE: View Website.

Please note in particular that not all successful applicants may be eligible to receive a full studentship (i.e. fees and stipend) - please read in detail the Residency and Citizenship requirements in the document linked to above.

How good is research at Queen’s University Belfast in Agriculture, Veterinary and Food Science?

FTE Category A staff submitted: 33.40

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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