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 firstname.lastname@example.org.
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: 17th September 2021.
Predictiva and DataLab PhD in Financial Technology Scholarship
This PhD will focus on developing artificial intelligence algorithms and other automation techniques for portfolio and risk management.
The successful candidate will develop unique academic research on building effective intelligent algorithms for optimal portfolio management, execution, and technical analysis. Also, they will help Predictiva translate the insights and useful features of this research into improving the effectiveness of Investiva, Predictiva’s proprietary trading agent.
Investiva is a machine learning agent which maximises the profits gained from trading financial markets using state-of-the-art deep reinforcement learning (DRL) algorithms. Inspired by arcade gaming strategies previously developed by DeepMind, it exploits a novel reinforcement learning approach that results in more profitable and less risky trades than standard buy and hold strategies and other baselines.
Eligibility – Academic Requirement
Meet the PhD in Financial Technology programme academic requirements. This normally requires a minimum qualification (or expected qualification if you are a current Master’s student) of above-average academic achievement, quantified as 70% or above overall at the Masters level, with a distinction level dissertation (or UK equivalent) in the subject of: finance, economics, informatics, physics, mathematics, engineering, or another relevant programme with significant quantitative elements.
Students with significant finance and tech industry experience or with relevant professional qualifications and that also have a minimum of a Bachelor’s degree in the programmes stated above will be given due consideration on a case-by-case basis.
Deadline for applications: 3:59 (GMT) on 27 August 2021.