Industrial doctorates


Industrial doctorates

Scotland needs highly–educated data experts, in research and business, that are capable of forging new ideas at the edge of what is currently achievable. 

We offer funding for Industrial Doctorate programmes to support the development of high level data science talent.

We co-fund industrial doctorates at Scottish Universities

We co-fund industrial doctorate programmes at Scottish Universities, in collaboration with Scottish industry or public sector organisations. These industrial doctorates are designed to support the development of data science talent at a PhD / EngD level, while facilitating collaboration between industry and academia through applied research projects.

We are unable to fund students directly.  Applications for funding must come from a Scottish University and be sponsored by an Industry or public sector Sponsor that has an operational base in Scotland.  If you require further information about this, contact

If you are a Scottish-based organisation or an academic institution and you are interested in developing a data-driven Industrial Doctorate project, have a look at our current Industrial Doctorates Call for Funding.

Open doctorate vacancies

Machine Learning and Expert Based System for Soft Fruit Yield Forecasting

About the Project

We are looking for a highly motivated student to work on a 3-year fully funded interdisciplinary PhD project on Machine/Deep Learning and Expert based system for soft fruit yield forecasting.

The project is a collaboration between the University of Aberdeen’s Department of Computing Science and Angus Soft Fruits Ltd (industry partner), and is supported by The Data Lab.

The position will be based in the Department of Computing Science, University of Aberdeen, but close collaboration with Angus Soft Fruits Ltd and farmers across Scotland is expected.

University of Aberdeen, established in 1495, is one of the oldest Universities in the UK and ranks among the top 200 Universities in the world (THE 2020).

According to the National Farmers Union, some 80% of Scotland’s land mass is under agricultural production, highlighting the importance of farming for the Nation’s economy. The overall turnover across farmers, crofters and growers is estimated at £2.9 billion a year and contribute to Scotland’s £5 billion food drink exports. In soft fruit industry alone, it is estimated that approximately 2,100 hectares are used for growing soft fruits, leading to an annual production of more than 2,900 tonnes of raspberries and 25,000 tonnes of strawberries.

Deep Learning theory/applications have seen an immense development in the past few years across a number of areas, such as convolutional and Capsule Neural Networks [1,2], Generative Adversarial Networks, Bayesian Deep Learning [3], etc. However, some open problems, such as improving performance whilst reducing model complexity, learning with few examples, neurosymbolic AI, etc., are some areas that further investigations are needed to move onto the next phase of deep/machine learning research that can inform novel and impactful applications (e.g. those with limited availability of data).

This project aims at developing and applying novel machine learning techniques for developing a system that can accurately predict/forecast strawberry yield [4,5]. Through our collaboration with Angus Soft Fruits Ltd and farmers across Scotland, the project will benefit from large amounts of data.

Areas of investigation might involve (but not limited to) data augmentation with generative models, causal inference, uncertainty estimation, time-series and tabular data analysis with deep learning techniques, intra-field yield variation, etc.

As with every PhD project, there is scope to shape the exact theoretical focus within Machine Learning so that a more accurate and robust yield forecasting system can be developed.

The student will work closely with other Researchers/PhD students within Dr Leontidis’s lab/Department and will have access to HPC/GPU facilities, and also funding for training/conferences. In addition, the student will further benefit from participating in activities and events organised by the Data Lab.

Find out more about the project

For any information or informal discussion please contact Dr Georgios Leontidis, Interim Director for AI and Data & Associate Professor (SL) in Machine Learning .


For further information, please contact the Skills Team.