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 email@example.com.
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
Robust and Explainable Machine Learning for FinTech Applications
To develop and compare Gaussian Process models with Deep Neural Networks to provide explainable and quantifiable Machine Learning for FinTech applications.
Deep Neural Network (DNN) technologies coupled with GPU type hardware provide practical methods for learning complex functions from vast datasets. However, their architectures are often developed using trial and error approaches and the resulting systems normally provide ‘black box’ solutions containing many millions of learnt but abstract parameters. They are therefore extremely difficult to interpret and understand, and their accuracy and certainty of prediction, or classification, are normally not known.
Consequently, DNNs are often not used for high-impact decision support, as management is rarely provided with sufficient, transparent evidence to engender confidence or allow assessment of risk.
In contrast, Gaussian Processes (GPs) can be designed using highly principled methodologies, in which human knowledge and assumptions are explicitly recorded and exploited to provide parsimonious machine learning solutions. Thus, the main aim of this project is to develop advanced statistical machine learning and visualisation methods for financial applications that can provide mathematically sound and explainable predictions.
Fully funded PhD position in association with Royal Bank of Scotland to investigate Robust and Explainable Machine Learning for FinTech Applications
Start date: September 2019
Stipend: £16k pa
Fees: Fully paid at UK, EU or Overseas fee rate
First Supervisor: Prof. Mike Chantler
Trustable Decentralised Applications on Reliable Blockchain Technologies
To design a suitable framework to support the development of reliable and trustable blockchain-based decentralised applications
Description of the project:
A fully funded PhD studentship on Trustable dapps on reliable blockchain technologies is available at the Computing Science and Mathematics division of the University of Stirling, UK, in collaboration with Wallet.Services.
The goal of this project is the design of a suitable framework to support the development of reliable and trustable blockchain-based decentralised applications. This project benefits from the participation of WalletServices (www.wallet.services), a well-established startup in the global fintech sector. WalletServices will provide use cases of interest and their industrial know-how to the project. The student is expected to carry out the research in collaboration with the company. The scientific project will take in consideration the latest developments in the technology, including, for instance, off-chain and multi-chain frameworks, tokenomics, proof of stake, blockchain programming and verification aspects. Specific interests and expertise of the student will also be taken into due consideration, as appropriate.
This project will be carried out under the joint supervision of Dr. Andrea Bracciali and WalletServices, within an international academic network with expertise in verification, game theory, cryptography, programming languages, modelling and finance, and will enjoy the support of a growing multidisciplinary group of researchers and students interested in blockchain technologies.
This project will also benefit from the thriving fintech Scottish sector, which has a strong interest in blockchain technologies, and could particularly contribute to the, academic or industrial, career development of the student.
Students with a background in, or across, computer science, economics, mathematics (non-exclusive list!), and interested in a scientific approach to breakthrough technologies are encouraged to apply. Exposure to formal verification, programming languages, game theory and/or understanding of “crypto-economics”, and/or competence in software development are a plus.
Application deadline: December 30, 2019
Start date: Negotiable
Stipend: £15,009 pa
Fees: Fully paid at UK or EU fee rate
If interested please email your CV to Dr. Andrea Bracciali firstname.lastname@example.org in first instance and add a couple of lines explaining why you are interested in this research.
First Supervisor: Dr. Andrea Bracciali