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Sheffield, United Kingdom University of Sheffield Full timeContract type: Open ended Location: Main Campus This is an exciting opportunity for a Lecturer in Artificial Intelligence at the University of Sheffield. Our research groups with strong AI interests, include Machine Learning, Speech & Hearing, Natural Language Processing and Visual Computing. We are seeking a candidate with an outstanding record of...
PhD Studentship
4 months ago
Supervisors: Prof. Solomon Brown (University of Sheffield) & Dr Mark Workman (Imperial College)
Socio-technical transitions are complex and subject to deep uncertainty. Indeed, topics previously considered to be subjected to scientific processes of `prediction' – such as the retail price inflation index - are now increasingly considered to be steeped in deep uncertainty. In-spite of long run datasets from the past there is increasing recognition that the ability to forecast is limited: data is sparse, inexact and the structure of economies shift over time i.e., the system is unbounded and complex adaptive. The transition to the net zero energy system requires a new set of tools and approaches as there is increased recognition that the existing toolkit is unable to accommodate deep uncertainty. This project seeks to develop a novel approach to work with deep uncertainty in the net zero transition for the UK electricity system.
With increased penetration of intermittent renewables, i.e. solar and wind generation, there is an increased need for the deployment of clean energy technologies, including energy storage and carbon capture and storage. The structure and behaviour of energy markets are key to maintaining drawing forward investment into these technologies to provide security of the UK's power system. The purpose of this project is to understand the dynamics of the different energy markets in the UK, and how these might be designed or policies implemented in order to encourage the deployment of these vital technologies.
This project will use agent-based modelling to simulate behaviours of the various actors involved in the energy markets, and will extend this by developing an agent-based approach to capacity expansion in the system. This model will be used as the basis for an explorative study to understand what role markets and various technologies might have in the context of the variety of uncertainties that must be accounted for. This will make use of techniques such as Real Options analysis to support quantitative decision making.
The candidate will require a 1st or 2:1 degree in Engineering, Physics, Mathematics or similar discipline with strong mathematical skills. You will join the Brown Group within the Department of Chemical and Biological Engineering at the University of Sheffield. You will benefit from the world class expertise of the research group and interaction with several related research programs. The project is part of a larger collaboration involving colleagues at Imperial College London and you will have the opportunity to collaborate with key Industrial partners as well as work on additional deep uncertainty projects on the frontier of financial risk management and climate security.
For more info please contact Prof. Brown:
- Workman, M.H.W., et al., 2020. Decision making in contexts of deep uncertainty—an alternative approach or long-term climate policy. In Environmental Science & Policy
- Financial Times dated 16th April 2024. Economic forecasting - little more than performance art for central bankers Ben Bernanke's review is full of sound recommendations but unlikely to alter this opaque process.
- S. Pye, et al 2021. Modelling net-zero emissions energy systems requires a change in approach, Climate Policy, 21:2, , DOI: /
- van Dorsseret al., 2018. Improving the link between the futures field and policymaking. In Futures –84
Funding
This studentship includes a fee bursary to cover the UK (home) rate, and an annual tax-free maintenance stipend at the standard UK Research rate (£19,237 in for up to 3 years.
£19,237 - please see advert
