Industrial doctorates


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. 

We offer funding for Industrial Doctorate programmes to support the development of high level data science talent.

We co-fund industrial doctorates at Scottish Universities

We co-fund 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.

We are unable to fund students directly.  Applications for funding must come from a Scottish University and be sponsored by an Industry or public sector Sponsor that has an operational base in Scotland.  If you require further information about this, contact

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.

Open doctorate vacancies

Building an Ecosystem of Digital Twins for FASTBLADE

This project is to develop and exploit an ecosystem of digital twins of the main components of the FASTBLADE Facility.

The School of Engineering at the University of Edinburgh is currently building a first-in-class EPSRC funded structural composites research facility (FASTBLADE) for fatigue testing of tidal turbine blades. This facility is mainly used to a) determine the static loading performance of the blade (stiffness-deflection curve plus full strain mapping of the surface in strategic sections of the blade, b) perform a cyclic loading test to 10 million cycles in cantilever mode. 

FASTBLADE can be divided into five systems that complex interact together in a single environment. These systems are:

1. FASTBLADE Reaction Frame FEA .

2. Hydraulic System (Pumps, pipe network and actuators).

3. Control System and Data Acquisitions System.

4. Cooler Network and Oil Conditioning System.

5. Building Information Management System.

These systems, and the fact that the facility is located in an industrial-academic setting, provide a unique opportunity to develop robust digital ecosystems of Digital Twins that can improve asset management, structural health monitoring, and industrial processes to deliver environmental, economic benefit. Create and combine the different digital twins into a digital ecosystem will be undertaken in this PhD research.

Deadline for applications: 8th November 2021.


For further information, please contact the Skills Team.