The UCL Energy Institute is recruiting a Lecturer in Energy and Artificial Intelligence. This role is suited for an emerging researcher with strong research in AI and energy, and experience in teaching. The successful candidate will contribute to the Institute's research and teaching activities, including the development and application of advanced machine-learning models and big data analytics in energy systems. You will support research collaborations that advance the field, contribute to applied energy solutions, and engage with stakeholders across academia, industry, government, and NGOs. You will also play an active role in the delivery of UCL's teaching programmes, helping develop students' skills in AI, data analytics, and sustainable energy systems. The successful candidate will develop an independent research agenda in AI for energy, contribute to teaching across MSc and BSc programmes Energy Systems and Data Analytics and Sustainable Built Environments, Energy and Resources BSc and MEng by teaching two modules.
This post is permanent and available from 1 September 2026.
A job description and person specification can be accessed at the bottom of this page.
If you have any queries regarding the vacancy, or the application process, please contact bseer-recruitment@ucl.ac.uk.
If you have specific questions about the role please contact Prof Catalina Spataru, c.spataru@ucl.ac.uk.
If you have any questions related to MSc programme ESDA please contact Dr Aidan O'Sullivan aidan.osullivan@ucl.ac.uk.
UCL welcomes applications from international applicants and has licence to sponsor individuals who require a visa. This is dependent on the post and candidate meeting eligibility requirements for visa sponsorship under UK Visas and Immigration legislation.
You are an internationally recognised researcher with strong expertise in artificial intelligence, machine learning, and data analytics applied to energy systems and sustainability challenges. You have deep experience in supervised and unsupervised learning methods, and in deploying deep learning architectures, with applications across energy systems, urban environment, transport or the built environment.
Your work may involve large-scale energy datasets and big data analytics, and you have demonstrated expertise applying AI/ML techniques to real-world energy problems. Experience in time-series modelling, simulation forecasting, reinforcement learning, and causal modelling approaches for evaluating the impacts of policy and technological interventions would be particularly valuable. You have a track record of producing research outputs in peer-reviewed journals and can demonstrate potential to develop an independent research programme. You are committed to teaching and student mentorship, with experience in delivering taught modules in AI, ML and data-driven approaches to energy systems, sustainability, and innovation.
You hold a PhD in a relevant field, such as artificial intelligence, data science, or energy systems, with a clear focus on applications in the energy sector. You have experience in research and teaching, and have contributed to applied energy research projects or collaborations that demonstrate the practical impact of AI and machine learning methods in energy systems.
As well as the exciting opportunities this role presents, we also offer some great benefits, some of which are below:
Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out more.
As London's Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world's talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We therefore particularly encourage applications from candidates who are likely to be underrepresented in UCL's workforce. These include people from ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women. Our department holds an Athena SWAN Silver award in recognition of our commitment and demonstrable impact in advancing gender equality.
Tagged as: Life Sciences
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