Context-based machine learning for financial applications

Cross-Faculty Research Programme: “CONTEXT: Contextual learning and processing”
Faculty of Natural Sciences – Computing Science and Mathematics
Stirling Management School – Accounting and Finance Division

Applications are invited for a PhD studentship in an interdisciplinary project on context-based machine learning with a specific emphasis on financial applications. The PhD will be undertaken under the joint supervision of the University of Stirling’s Computing Science and Mathematics Division and its Accounting and Finance Division.

The PhD studentship is part of a new cross-faculty, interdisciplinary research programme (RP) called “CONTEXT: Contextual learning and processing” which concerns understanding and applying principles of contextual guidance in learning and processing of data. It combines research into the role of context in the human neocortex with computational and machine learning. This RP studies context-sensitive learning and processing from three perspectives: neuronal architecture and physiology; health and pathology; the development of new, robust algorithms for machine learning for real-world applications. This studentship will investigate the development of such algorithms specifically in financial applications. The PhD thus offers a unique opportunity to work with researchers from computing science and mathematics, the cognitive and neurosciences, and finance, all of whom are committed to the development and deployment of new approaches in machine learning.

As the PhD scholarship is co-funded by a B2B Robo Advisor firm based in Singapore, researchers have the opportunity to directly impact the use of machine learning in real-world financial applications, and there is also the possibility to gain direct experience working with a leading financial-technology company.

The student will enjoy the benefit of supervision from two faculty members spanning two academic disciplines as well as access to the research environment in these two disciplines. The candidate will also have access to facilities and support offered by Stirling University’s Graduate School and the Centre for Graduate Research in Management.

Further details can be provided by Professor Bruce Graham from Computing Science ([Email Address Removed]) or Dr Patrick Herbst from Accounting and Finance ([Email Address Removed]).

Application requirements:
Candidates are expected to have a good upper second class honours degree or equivalent in a subject relevant to the field of study and/or a distinction or near distinction in a Master’s degree. An MRes or equivalent degree with a high level of research training content would be an advantage. Applicants should have:
- A strong background in computing and mathematics (an understanding of machine learning is highly desirable)
- An ability to programme in a high-level computing language (such as Python, C or Java) and/or experience with Matlab (or similar)
- An interest in financial applications and research (a background in finance is desirable, but not essential)
- Good numerical and verbal communication skills
English language requirements are IELTS 6.5 (with a minimum of 6 in each skill), TOEFL 577/233, or equivalent.

Application process:
In the first instance students should make a preliminary application by e-mail to [Email Address Removed] with the subject line: "PhD Studentship in Context-based Machine Learning ". The application must include
- a full CV, documenting qualifications and any relevant previous experience and existing skill sets as well as containing contact details for two academic referees;
- a covering letter of max 500 words referring in detail to the relevant research area and demonstrating a strong interest in the proposed research, as well as the candidate’s aptitude/skills that make her or him particularly suitable for conducting this research;
- copies of your academic transcripts.
The CV and covering letter or research proposal will be used to shortlist candidates for interview. The successful candidate must subsequently apply for a place on the university’s PhD programme; the scholarship offer is conditional on the student being accepted for this.

Closing date: 16 June 2017
Proposed start: 1 October 2017