The scholarly study of science, innovation and entrepreneurship (SIE) is a crucial field in advancing the
understanding of SIE dynamics, providing evidence-based policy advice and guiding the practice. The field
has grown rapidly in recent decades with specialist top ranking journals and conferences, academic
departments and a vibrant community. Today the world faces complex issues such as ever-accelerating
technological change, increasingly skewed distribution of welfare gains from technologies and innovation,
intertwined sustainability and responsibility issues related to emerging technologies, and growing societal
grand challenges such as climate change, inequality and poverty. On the face of these challenges, the field
is still confined to the traditional boundaries of established disciplines such as economics and
management. In recent years, there have been multiple calls that the scholarly field of studying SIE is in a
crisis as the existing approaches have reached their “intrinsic limits” (Soete, 2019; Burmaoglu & Saritas,
2019; Martin, 2016; Martin, 2012).
One of the ways forward proposed to address these challenges is to embrace the recent advances in data
science, which is fundamentally affecting all scholarly fields across the board. Data science unlocks data
sources that were not previously conceived, equips scholars with the ability to replace traditional
simplistic models of the world with more nuanced approaches and enables rapid response to the
emerging phenomena. All these capabilities, in turn, provide opportunities to increase the rigour,
relevance and timeliness of such studies as well as their relevance of the existing debate and theoretical
frameworks. These opportunities are increasingly being recognised and captured by scholars in the field,
evidenced by large research programmes supported by research funders and increasing share of data science skills in researcher training in top universities.
We welcome applications that study management and policy aspects of science, innovation and entrepreneurship by employing advanced data science approaches and/or utilising new data sources. Applicants are required to put forward an outline research proposal that includes a clear research question and a relevant methodology. While the opportunity is open to any research proposal within this broad remit, we are particularly interested in proposals on the topics we have recently been working (science system transitions, social innovation, innovation policy, entrepreneurship policy).
You will be registered to the PhD in Entrepreneurship programme based at Strathclyde Business School, under the supervision of Dr Abdullah Gök. You will be a part of Centre for Doctoral Training in Data Science for Studying Science, Innovation and Entrepreneurship.
For any questions or informal enquiries, please contact Dr Abdullah Gök.
How to apply
At this stage, we are inviting applicants to apply for the scholarship only. The successful candidate will then be asked to complete an application for PhD study at Strathclyde.
All applications should include:
- A cover letter indicating the candidate's relevant skills/experience and how these can contribute to this research
- An outline research proposal on the broad topics mentioned in project details, including the problem statement, possible research questions, methodology and proposed academic and policy/practice contributions (min 2 pages).
- An up-to-date CV
- Relevant qualification transcripts (e.g. undergraduate and/or postgraduate degree)
- Two references (please refer to guidance on references)
- (Optional) English language test results (can be completed later)
Applications with missing documents will not be assessed.
When sending the above documents please use the following file-naming convention: fullname_typeofdocument
Apply now by uploading your documents.