About The Role
The Science Policy Research Unit (SPRU) at the University of Sussex is seeking 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, and to generate knowledge and insight for a more just twin transition. Your contribution will include:
* Studying historical shocks to regional economies, including those from technological change and global trade.
* Analysing business and employee data to trace the economic impacts of shocks.
* Using natural language processing (NLP) to analyse large‑scale textual data (e.g., data extracted from the Global Database of Events, Language and Tone, GDELT) to identify policy actions and responses.
You will also work within the project team and communicate with PACT partners at leading European universities and research institutions:
Aristotelio Panepistimio Thessalonikis (Greece), Centre for European Policy Studies (Belgium), Centre for Economic and Regional Studies (Hungary), Complexity Science Hub (Austria), Gran Sasso Science Institute (Italy), Universiteit Maastricht (Netherlands), University of Ferrara (Italy), and University of Gothenburg (Sweden).
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 and some experience in combining qualitative research methodologies with quantitative approaches. You will manage your workload independently and support colleagues when needed. Your communication skills should enable clear interaction 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.
* Contribute to the writing of academic papers, reports and blog posts.
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. The University requires that work undertaken for the University is performed in the UK.
Benefits & EEO
The University of Sussex values the diversity of its staff and students and welcomes applicants from all backgrounds. Please find further information regarding the University of Sussex Business School on our website. Find out about our equality, diversity and inclusion. Find out about our reward and benefits package.
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