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. The Data Lab offers funding for Industrial Doctorate programmes to support the development of high level data science talent.

The Data Lab co-funds 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.

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.

The following fully funded vacancies are now open for prospective doctoral (PhD / EngD) students. For further information please contact

Farm incomes data project (EngD)

University Partner: University of St Andrews

Industry Sponsor: The Scottish Government and NHS National Services Scotland

Initially the Research Engineer (RE) will work on a farm incomes data project with the Scottish Government Rural and Environment Science and Analytical Services (RESAS) to:

  • consider potential sources of information to substitute/complement on-site data collection
  • design farm business income models/forecasts/benchmarking using data mining techniques to overcome time lag of results
  • design and test/implement a system for processing/assessing raw financial data to help inform future options for farm incomes analysis
  • potentially apply similar techniques to different areas within RESAS.

This will require using a broad range of data science techniques from the use of predictive analytics, analysing data using advanced quantitative methods, database management, linked data and/or complex datasets. This will give the RE a good understanding of data ethics from working on these projects.

The RE will then work with NHS National Services Scotland on:

  • mining datasets for assessing quality and building algorithms for predictive actionable insights
  • enhancing the statistical disclosure control process with algorithms for minimising the risks of revealing identifiable data.

The RE will also get an opportunity to work on projects with other parts of the Scottish Government and the NHS on other data science related projects during their studies.

More Information Apply here

Large-Scale Data Processing using Heterogeneous Parallel Systems (EngD)

University Partner: University of St Andrews

Industry Sponsor: Codeplay Software Ltd.

Codeplay Software Ltd is an independent company that is based in Edinburgh. Codeplay has delivered standards-compliant systems for some of the largest semiconductor companies in the world, focusing specifically on high-performance heterogeneous processor solutions for CPUs, GPUs, DSPs, FPGAs and other specialized imaging and vision processors. Working within The Khronos™ Group to define new open standards such as OpenCL™, SPIR™, SYCL™, and Vulkan®, and leading the creation of new System Runtime and Tools standards through the HSA Foundation, Codeplay has earned a reputation as one of the leaders in compute systems.

This project will investigate large-scale data processing using heterogeneous parallel processing systems. Self-driving autonomous vehicles and other AI applications, such as natural language processing, will generate massive amounts of data from a large number of sources (e.g. multiple cooperating vehicles in a city). The problem is to collate, analyse and process this data quickly and effectively. The project will study advanced algorithms that can effectively exploit new heterogeneous parallel processing systems for this purpose (comprising e.g. a mixture of CPUs, GPUs, DSPs and FPGAs). This will involve embedded processing, centralised processing (e.g. to collate/analyse data from multiple distinct sources) and/or peer-to-peer processing (for information sharing, to allow better use of computing resources, or to support e.g. flocking-style behaviours from multiple cooperating autonomous systems).

We would expect a successful applicant to have experience of:

  • Parallel Programming
  • Programming language implementation
  • Heterogeneous parallel systems (CPU, GPU, FPGAs) (optional, but an advantage)
  • Artificial intelligence (optional, but an advantage)
  • Handling large volumes of data (optional, but an advantage)

The successful RE will work in our office in Edinburgh, as part of the research team supervised by Uwe Dolinsky.

More Information Apply here

Predictive Analytics for Short-term Wind and Solar Power Forecasting

University Partner: Strathclyde University

Industry Sponsor: Natural Power

Academic Supervisors: Dr Jethro Browell

This PhD aims to develop improved forecasting methodologies by exploiting contemporary statistical methods for processing large quantities of explanatory data including numerical weather predictions and the wide range of measurements made a wind and solar farms, many of which are available in close to real-time. This PhD would suit candidates with a background in mathematics, statistics, computer science, meteorology, or other numerate disciplines.


  • 3.5 year PhD with negotiable start. Interviews for short-listed candidates.
  • The studentship comprises a competitive stipend (£16,000/year, tax free), tuition fees (for EU-applicants only) and travel expenses.
  • Project partnership with Natural Power, who will provide industrial supervision, training and context. The student will be expected to work for extended periods at Natural Power offices in Stirling and/or Castle Douglas, to be agreed with the student.
More Information