The studentship is entitled Enhancing labour market intelligence using machine learning. It is suitable for holders of undergraduate or Masters degrees in subjects such as Business Information Systems, Business Studies, Computing, Data Science, or Information Science, who have interests in one or more of the following:
- Artificial Intelligence/Machine Learning/Natural Language Processing
- Business Information Technology
- Data Science
- Information Science
- Labour Market Intelligence
The success of Labour Market Intelligence (LMI) for forecasting is predicated on accurate, reliable, robust, and accessible data that underpins the decision-making process. Developing, processing, and maintaining vast quantities of often unstructured data (such as text or time-series data) is extremely difficult.
The work to be undertaken for this doctoral study focuses on the application of novel and explainable artificial intelligence (AI) approaches to enhance the current provision of LMI, with a particular reference to skills planning, forecasting and investment in training provision.
In the initial stages of the research, identification of existing and potential applications of machine learning and Natural Language Processing to enhance LMI will be explored. Thereafter, identification of suitable data sources for predictive purposes will be investigated, drawing upon existing Skills Development Scotland (SDS) data and previous research. The PhD candidate, with guidance from the supervision team, will explore these opportunities to develop novel machine learning models that represent a move towards dynamic forecasting capabilities that can be evaluated through ethical and explainable machine learning approaches.
Completion of the project will contribute new knowledge through the provision of state-of-the-art models for LMI forecasting, natural language processing and information representation. This proposal is highly innovative: It extends and enhances the scope of current LMI forecasting; It supports improvements to the My World of Work portal and the accuracy and relevance of data to users; whilst strengthening the SDS Strategic Frameworks beyond 2022 by ensuring that the latest evidence underpins future frameworks.
Four staff from Napier’s School of Computing and Business School will supervise the student. They are: Director of Studies Dr David Brazier (Computing), Dr Dimitra Gkatzia (Computing), Dr Matthew Dutton (Business School), and Alistair Lawson (Computing).
The funding includes fees, a stipend, and training budget for either three or four years. Home students (only) are eligible apply. (See the ESRC eligibility information.)
- The +3 programme is for those who already have a Masters degree with a strong focus on research methods, and are qualified to start their doctoral studies in October 2021
- The 1+3 programme includes a first year on a Masters degree in research methods in 2021/22, followed by three years of doctoral studies from October 2022.
This studentship is an ESCR/Skills Development Scotland (SDS) collaborative award. It is offered through the Science, Technology and Innovation Studies and Information and Communication Studies pathway of the Scottish Graduate School of Social Science (SGSSS).
Our track record of ESRC/SDS collaborative awards
The Centre for Social Informatics has a strong track record of supporting ESRC/SDS-funded PhD students. We have four students currently on the programme, and two graduates: Dr John Mowbray 2018 – now a Research Officer at the Scottish Government; Dr Lyndsey Middleton 2020 – now an Assistant Statistician, also at the Scottish Government.
We have recently recruited another student to the programme, who will also start in October 2021 alongside the successful candidate for this advertised studentship.
The recruitment timetable for the studentship is as follows:
- Applications are due by 4pm Monday 21st June 2021
- Notifications of interview will be issued on or before Wednesday 23rd June 2021
- Interviews are scheduled for Monday 28th June 2021 (online)
- The studentship will start in October 2021
- As a three year programme, if the appointed student already holds a Masters degree with at least 60 credits of research methods training
- As a 1+3 four year programme, if the appointed student do(s not already hold a Masters degree with at least 60 credits of research methods training, with an MSc(R) undertaken as the first year, followed by three years of doctoral study
The studentships can be taken full-time or part-time. Only home students are eligible to apply. For full details including the application process, please see the full advertisement on the SGSSS web site.