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

Effective authentication decisions from multiple data components

University Partner: Heriot-Watt University

Industry Sponsor: Payfont

Academic Supervisors: Dr. Mike Just (Heriot-Watt), Dr. Michael Lones (Heriot-Watt) and Dr. Lu Fan (Payfont)

The aim of this 4-year, fully funded project is to design and implement new methods for making effective authentication decisions from multiple data components, which will consist of two main stages: (i) Designing and implementing new, implicit forms of authentication that involve learning from a person’s behaviour, and (ii) Designing and implementing methods for combining and scoring multiple forms of authentication. The resultant methods would contribute to Payfont’s IOMI (I Own My Identity) technology framework. The student will spend part of their time at Heriot-Watt University, and part at Payfont, for the duration of their studies.

To apply for this position, please complete the application form by 15 May 2017. When completing the application form, be sure to specify “Dr. Mike Just” as the project supervisor, and specify “Data Lab funded” as the source of funding. Applicants are requested to send an email to Dr. Mike Just once they have submitted their application.

More Information Apply here

Medical image analysis (EngD)

University Partner: University of St Andrews

Industry Sponsor: Toshiba Medical Visualization Systems (TMVS)

Academic Supervisors: Dr Keith Goatman

This project will apply methods from data science, including deep learning and convolutional neural networks, to medical image analysis. Specifically, we wish to identify specific tissue types in the images. For example, can we determine whether a lump is cancer or not without resorting to large biopsy needles? More subtle challenges include being able to distinguish different types of cancer, which is vital in order for doctors to select the best treatment. We could also use the tool to examine the health of important organs such as the lungs, liver and heart. The final aim of the project is the development of a novel clinical application based on these methods.

We would expect a successful applicant to have experience of:

  • Programming, including some experience implementing analysis algorithms (C++, Python, or similar)
  • Machine learning
  • Image analysis (optional, but an advantage)
  • Handling large volumes of data (optional, but an advantage)

The successful RE will work in Edinburgh, as part of the image analysis research team supervised by Dr Keith Goatman. This is a multidisciplinary team consisting of scientists, software engineers, clinical experts, and several EngD students.

More Information Apply here

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.

We would expect a successful applicant to have a strong background in Statistics, Computer Science or a related discipline, with good programming and systems skills.

The projects that the RE will work on during their studies will investigate and propose unique solutions within key policy areas of the Scottish Government and the NHS, with the aim of providing tangible real-world improvements to the value of public spending and policy making.

Through the placement, the RE will have the opportunities to work with and present their work to industry experts, government and potentially international peers.

More Information Apply here

generatiVe dEep neuRal networkS for interactiOn with autonomous systems (VERSO)

University Partner: Heriot-Watt University

Industry Sponsor: SeeByte Ltd

Academic Supervisors: Dr. Helen Hastie and Prof. Yvan Petillot

The goal of VERSO is to investigate interactive, data-driven methods for communicating with UxVs through a multimodal interface, i.e. graphics and chat (with the possibility of extending to speech in the future). The PhD will leverage the large body of work on Deep Neural Networks (DNN) for conversation, that learn how best to respond given example interactions rather than hand-crafting interaction rules.

More Information Apply here

Holistic, Data-driven, Service and Supply Chain Optimisation

University Partner: Robert Gordon University

Industry Sponsor: British Telecom

Academic Supervisors: Prof. John McCall and Dr. Olivier Regnier-Coudert

The project will apply holistic optimisation to operational service and supply chains. This is highly appropriate because such chains naturally give rise to linkage between optimisation problems where the solutions, timing and constraints of a number of different problems in the same chain interact with each other.

Closing Date: 12 noon Monday 12th June 2017

More Information Apply here

Trustable dapps on reliable blockchain technologies (PhD)

University Partner: University of Stirling

Industry Sponsor: Wallet Services (Scotland) Ltd.

Academic Supervisors: Andrea Bracciali and Leslie Smith

This project will adopt an innovative multi-disciplinary approach, bringing together the "economics" of blockchains and dapps, with the software frameworks used to design, develop and run them.