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About the role
The Science Policy Research Unit (SPRU) at the University of Sussex is seeking to appoint a highly talented and motivated researcher to join a Horizon Europe-funded project called, Past and Current Transitions: A new pact for fair and resilient European regions (PACT). You will work under the joint supervision of Dr Carolin Ioramashvili, Professor Maria Savona and Professor Daniele Rotolo.
The PACT project aims to analyse the social and economic consequences of the so-called ‘twin transition’ towards digitalisation and green transformation in European regions; regions already shaped by earlier socio-economic transformations, notably moves from manufacturing to service economies. The project mixes quantitative and qualitative methods in a combined historical and contemporary study that will generate knowledge and insight for a more just twin transition. Your contribution will include:
- studying historical shocks to regional economies, including from technological change and global trade;
- analysing business and employee data to trace the economic impacts of shocks;
- Natural Language Processing (NLP)-based analysis of large-scale textual data (for example, data extracted from the Global Database of Events, Language and Tone, GDELT) to identify policy actions and responses
About you
Ideally you will hold or be close to completing a PhD in a relevant field, such as economics, innovation studies or economic geography. You should have excellent skills in quantitative methods but also some experience in combining qualitative research methodologies with quantitative approaches. You will have the skills to manage your own workload and at times, support other colleagues. You will communicate clearly with the team, line management, and stakeholders. Specifically, you should be able to:
- Work with large, business and individual level datasets to identify economic shocks, and analyse changes in industry and employment structures.
- Use causal inference methods to identify the impacts of shocks on businesses and individuals, including productivity, employment and labour mobility.
- Process large-scale text corpora using NLP and related techniques to classify content and extract entities and relations among them.
- Contribute to the writing of academic papers and shorter outputs such as reports and blog posts.
About our Faculty
Please find further information regarding the University of Sussex Business School on our website.
Why work here
Find out more about our reward and benefits package.
Find out about our equality, diversity and inclusion.
Further Key Information
Please contact Carolin Ioramashvili (c.ioramashvili@sussex.ac.uk), Maria Savona (m.savona@sussex.ac.uk) or Daniele Rotolo (d.rotolo@sussex.ac.uk) for informal enquiries.
Please include a motivation letter, CV, contact details for two referees and a writing sample, such as a journal publication or draft PhD chapter with your application.
Eligibility
Visa Sponsorship Queries: This role has been assigned an eligible SOC code and meets the salary requirements for Skilled Worker Sponsorship if full time and appointed at Grade 7.4. Please consult our Skilled Worker Visa information page for further information about Visa Sponsorship.
The University requires that work undertaken for the University is performed in the UK.
