PhD Study : Creating opportunities for growth within the circular economy - predicting and minimising food waste

Summary

This study will focus on driving efficiencies across the food supply chain by creating a predictive tool to reduce food waste among retailers. Combining big data relating to inventory, sales and food waste alongside other uncontrollable factors (e.g. weather conditions, transport strikes, changing consumer behavior, etc.), a predictive tool will be developed to support managerial decision-making on how to effectively redistribute and reuse food, that may otherwise be wasted.  Focusing on fresh food waste, specifically, fruit and vegetables, this project has the following key objectives/stages:

1.Context setting – Review of current UK government/policies on Food Waste.

2. Systematic Literature Review – Identification of controllable and uncontrollable factors impacting on food waste prediction within retailing.

3.Qualitative – mapping the fresh food supply chain across one food retailer.

4.Quantitative - Development of database – integration of big data across each stage of the supply chain and additional data on external factors (e.g. weather, transport strikes, changing consumer behavior, etc).

5.Quantitative - Development of AI algorithms - predictive analytics will be used to develop algorithms which will accurately forecast the potential for food waste within the fresh fruit and vegetable category.

6.Testing predictive tool – the tool will be tested in a retail setting. Perceptions of the usefulness of this tool for informing managerial decisions will be explored and used to inform the final stage of the study.

7.Recommendations – a set of managerial and technical recommendations will be made to improve the future viability of applying the algorithm to other categories within the food retail supply chain.

Advanced AI algorithms will be developed on data supplied by our industrial partner Sonae Retailing, a leader in this area who has published a White Paper "The Future of Food” which outlines key recommendations on how EU and national policymakers can help foster innovation and cooperation in the food sector.

This successful candidate for this PhD project will work within Intelligent Systems Research Centre (ISRC) and focus on innovations in intelligent systems and data analytics to develop novel analytical methods in one or more the following areas  :

*Self-organising fuzzy neural networks

*Mixtures of neural experts

*Hidden Markov Models

*Monte Carlo Methods

*Bayesian Networks

*Reinforcement learning

*Predictive modelling

The project will therefore involve contributions to fundamental methods and validation of these methods on challenging real-world datasets. The PhD candidate will have access to state-of-the-art hardware and software for data analytics, a high performance computing facility, as well as computational modelling techniques and will be integrated within and learn from the Cognitive Analytics Research Laboratory (CARL) team of data scientists and engineers with specialist knowledge in various domains as well as from multi-disciplinary teams of researchers at the ISRC. There is significant demand for expertise in data analytics. The PhD opportunity will enable the successful candidate to gain that expertise and to push the boundaries on the state-of-the-art, and apply their knowledge to develop solutions to challenging industry led problems that will have a significant global impact.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain

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%
• For VCRS Awards, Masters at 75%
• Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living