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 skills@thedatalab.com.



Machine learning approaches to improve retrieval of shelf sea algal biomass from ocean colour remote sensing

Algal primary production in the marine environment constitutes approximately half of the global total, is therefore a very important element of the global carbon cycle and is the primary source of nutrition for the vast majority of life in the ocean. Various pieces of environmental legislation (EU MSFD, Water Directive etc) require governments to monitor the ecological state of national waters, with algal biomass being used as a proxy for eutrophication. There is also growing interest in monitoring for the presence of harmful algal blooms due to their potential impact on both aquaculture and public health more generally. Ocean colour remote sensing has radically transformed our ability to observe the growth and decay of algal blooms across the globe. However, the performance of standard algorithms for monitoring algal biomass is notoriously variable, with significantly lower performance in optically complex shelf seas. The aim of this project is to use state of the art machine learning approaches to improve understanding of local variability in the optical properties of natural waters and hence to inform interpretation of both historical ocean colour imagery and existing databases of in situ measurements of chlorophyll concentration. This will facilitate construction of a new, water-type specific approach to estimation of algal biomass for Scottish marine waters that will be integrated with regional hydrodynamic and ecosystem models to provide Marine Scotland and other Scottish public bodies with new tools for monitoring and predicting ecosystem status.

This project is jointly funded by the Data Lab and MASTS Industrial Doctorate program and by the University of Strathclyde. The successful candidate will be based at the University of Strathclyde in the Physics Department but will work with a range of experts in machine learning (Dr Jinchang Ren, EEE, Strathclyde), remote sensing (Dr Jacqueline Tweddle, University of Aberdeen) and with Scottish Government scientists (Drs Alejandro Gallego, Matthew Gubbins and Eileen Bresnan, Marine Scotland, Aberdeen). The PhD is open to EU nationals and is fully funded for a total of 3.5 years, with preferred start date of 1st Oct 2018.

Contact David McKee for further information.

Dynamic spatial modelling and forecasting of sea lice abundances

At the present time, we are able to forecast physical conditions (meteorology and current patterns), and resulting dispersal patterns of larval lice, but a lack of site data has prevented development of understanding of the mechanisms for sea lice population dynamics. Over the last year, Marine Harvest have published this data on a monthly basis for their sites. Working with this data will allow parameterisation of a predictive model for lice abundances, including environmental and management factors. This will allow the development of a forecasting tool that may be used to forecast lice abundances at a regional scale and at fine temporal resolution. Development and validation of such a model would represent a huge leap forward in terms of our understanding of the parasite, but would also offer huge potential benefits to the industry in the longer term, allowing reduced treatment costs and lower environmental impacts.

  • Deadline: June 28, 2018 @5pm BST
  • Start date: October 1, 2018
  • Supervisor: Dr Thomas Adams

Students must be domiciled in the Highlands and Islands transition region during the course of their study to be eligible for funding. Applicants must possess a minimum of an Honours degree at 2:1 and/or a Master’s Degree (or International equivalent) in a relevant subject.

More Information

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