Eligibility

To be considered for this PhD position, applicants must have obtained an excellent Master’s degree in Economics or related quantitative fields (e.g., Statistics, Data Science, Quantitative Finance, etc.). A strong interest in and significant knowledge of time series econometrics, excellent analytical skills, proficiency in statistical software programming (preferably R or Matlab), and a curiosity in exploring new Bayesian machine and statistical learning techniques are essential.

The successful candidate will be expected to conduct both independent and collaborative research, contribute to developing and improving research and econometric methods, and develop their own objectives and proposals, all under the guidance of the supervisory team (Dr Hauzenberger, Professor Koop, and Professor McIntyre). Excellent communication skills (both written and verbal) are essential to present econometric output and complex information (e.g., in the form of econometric or machine learning output) in an accessible way to both experts and non-specialists. When reviewing the application materials, we will particularly value evidence of your ability to conduct independent research, as demonstrated by a quantitative analysis or an econometric project (preferably in macroeconomics or finance) undertaken during your studies.

Strathclyde Business School is committed to supporting a diverse and inclusive postgraduate research population. We make decisions on entry by assessing the whole person and not relying solely on academic achievements. On that basis, please ensure that your application (via your CV and covering letter) can evidence your resourcefulness, commitment and resilience as demonstrated by broader professional and life experiences. This evidence should be centred on your ability to undertake and complete a PhD and contribute to a positive PhD community.  

If English isn't your first language, you'll need an IELTS score of 6.5 or equivalent with no individual element below 5.5.