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

Robust and Explainable Machine Learning for FinTech Applications

To develop and compare Gaussian Process models with Deep Neural Networks to provide explainable and quantifiable Machine Learning for FinTech applications.

Description of the Project: 

Deep Neural Network (DNN) technologies coupled with GPU type hardware provide practical methods for learning complex functions from vast datasets.  However, their architectures are often developed using trial and error approaches and the resulting systems normally provide ‘black box’ solutions containing many millions of learnt but abstract parameters. They are therefore extremely difficult to interpret and understand, and their accuracy and certainty of prediction, or classification, are normally not known.

Consequently, DNNs are often not used for high-impact decision support, as management is rarely provided with sufficient, transparent evidence to engender confidence or allow assessment of risk.

In contrast, Gaussian Processes (GPs) can be designed using highly principled methodologies, in which human knowledge and assumptions are explicitly recorded and exploited to provide parsimonious machine learning solutions. Thus, the main aim of this project is to develop advanced statistical machine learning and visualisation methods for financial applications that can provide mathematically sound and explainable predictions.

Fully funded PhD position in association with Royal Bank of Scotland to investigate Robust and Explainable Machine Learning for FinTech Applications

Start date: September 2019
Stipend: £16k pa
Fees: Fully paid at UK, EU or Overseas fee rate

Project description:

If interested please email your CV to in first instance and add a couple of lines explaining why you are interested in this research. Please also cc

First Supervisor: Prof. Mike Chantler

For more information and to apply.


For further information, please contact the Skills Team.