EPSRC PhD Studentship: AI-Driven Labour Markets Resilience

Level: Postgraduate research (Edinburgh campus; full-time)

Entry date: September 2026 or January 2027

Closing date: Wednesday 12th August 2026

Edinburgh Business School at Heriot-Watt University is offering two full-time PhD studentships funded by the Engineering & Physical Sciences Research Council (EPSRC) to start in the 2026-27 academic year (either September or January entry is possible). The studentships include a tuition fee waiver for UK fees only, an enhanced annual stipend currently set at £23,985 (10% above the standard UKRI rate), and an expenses allowance of £3,500 for training and research costs.

The duration of the studentships is 3.5 years. Overseas students are eligible to apply, but must be able to demonstrate how they would cover the difference between the UK and overseas fees.

We invite applications from candidates who can address one of the following topic areas in their PhD:

AI and Causal Inference for Labour Market Resilience. Artificial intelligence is transforming labour markets, yet credible evidence on its long-term economic impact remains limited by the absence of robust measures of AI adoption, diffusion, and labour market adjustment. This PhD project will develop new empirical approaches to understand how AI-driven technological change reshapes the structure and resilience of labour markets by combining large-scale vacancy data with modern natural language processing, network analysis, and causal inference methods.

Rather than focusing solely on task automation, the project will investigate how labour markets adapt (or fail to adapt) to AI-driven shocks, with particular attention to regional resilience, labour market coordination, and inequality. Specific applications may include constructing geographically granular measures of AI exposure and adaptation capacity at the commuting-zone level to analyse impacts on employment dynamics, wages, worker mobility, and regional divergence, as well as evaluating whether the diffusion of large language models (LLMs) is itself reshaping labour demand by changing hiring behaviour, accelerating skill volatility, and altering the coordination mechanisms through which workers and firms are matched.

By integrating scalable measurement with credible causal designs, the project will generate novel evidence on how AI adoption propagates through labour markets and how economies adjust to rapid technological change, contributing to both methodological advances and policy-relevant insights on labour market resilience and inequality.

The supervisory team for this project area will consist of Prof. Cristina Tealdi and Dr Yannis Kostas. Candidates interested in this project area are encouraged to contact Prof. Tealdi () for an informal discussion about their research proposal prior to submitting their application. When submitting your application for this project area, please select the option ‘Economics PhD’ from the drop-down list on the online application system.

In addition to the standard entry criteria listed below, applicants for this project should note that a Master’s degree in Economics, Statistics, Mathematics or Computer Science is essential.

Financing the Circular Economy Transition. The transition towards a circular economy (CE) is increasingly recognised as essential for achieving net-zero targets, strengthening supply chain resilience, and reducing dependency on critical raw materials. However, significant financial, technological, and institutional barriers continue to constrain the uptake and scaling of circular business practices and investment. This PhD project will investigate how finance and investment can accelerate the CE transition across areas such as electronics, cooling, heating, and hard-to-recycle materials.

Potential research topics include the financial performance of circular business models, sustainable and transition finance, circular supply chain investment; climate-related financial risks; ESG and sustainability disclosure; and the role of institutional investors in supporting circular innovation and resource security. The project may also examine emerging issues such as AI-enabled and agent-based…