The PILLARS project will study how changes in emerging automation technologies, their adoption along GVCs, and industrial transformations, affect the future of work, including through the reconfiguration of the demand for skills.
The Research Fellow will primarily focus on measuring which automation technologies are more likely to advance in the near future. They will use data mining, machine learning, and text analysis methods to analyse unstructured data on Science, Technology and Innovation (STI) to estimate the industry exposure to such emerging automation technologies. They will combine STI data with available data on companies websites and online job vacancies to provide an unprecedented analysis on how technology adoption and skill demand will evolve.
The Research Fellow will work in collaboration with a team of top innovation scholars, economists, and job market data scientists in SPRU and partner organisations, and two Research Fellows that will be recruited in due course. Funding will be available to recruit Research Assistants to support data collection and analysis, participate in several projects meetings and workshops to refine methods and data, as well as external conferences and workshops. As part of PILLARS, they will also have access to several data sources on job markets, companies, as well as STI.
The ideal candidate, will have a PhD in Economics or equivalent disciplines, with a strong curriculum on data science. They will have curiosity to explore new data and methods, and wiliness to work in an interdisciplinary, collaborative, and stimulating team and project. They may have a background on the analysis of STI corpora, and other unstructured data, and a background on economics of innovation theories, especially in relation to employment and the organization of work and industries. Ability to think about new ways of combining data to explore the dynamics of technologies is welcome.
Closing date for applications: 21 May 2021
Further details and how to apply: https://www.sussex.ac.uk/about/jobs/rf-econ-innovation-data-science-ref-5634