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

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