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

Industrial Doctorate PhD Research Studentship: Deep Learning and Computer Vision for Automated Visual Inspection

Glasgow Caledonian University - School of Engineering and Built Environment

  • Qualification type: PhD
  • Location: Edinburgh
  • Funding for: UK Students, EU Students, International Students
  • Funding amount: £16,000 annual stipend + covers tuition fees
  • Hours: Full Time
  • Placed on: 16th February 2018
  • Closes: 16th March 2018
  • Reference: GCU/SEBE/GMorison/001

Opportunity for an Industrial Doctorate PhD Studentship in: Deep Learning and Computer Vision for Automated Visual Inspection

Applications are invited for a full-time Industrial Doctorate PhD Research Studentship with the School of Engineering and Built Environment at Glasgow Caledonian University and Geckotech Solutions Ltd. The studentship is for a period of four years and covers payment of tuition fees and an annual stipend of £16,000 along with a substantial training/travel budget. As this is an Industrial Doctorate PhD position the successful candidate will be situated in the Research and Development Department at Geckotech Solutions Ltd.


The aim of this project is to assess and extend upon the state of the art in Deep Learning and Computer Vision for Structural Monitoring. Initially this will require the development and real time implementations of Deep Learning based algorithms on GPU processors for automated visual inspection. These algorithms will then subsequently be extended to apply Deep Learning techniques in real time for classification and segmentation of 3D point cloud data.

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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.
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