Location
London (Hybrid working pattern)

Hours
Full Time

Salary
£54,931 - £64,644 per annum

About the Role
The UCL Energy Institute is seeking a Lecturer in Energy Data Analytics and Sustainability with expertise in advanced data analytics, machine learning, and AI applications within the energy sector. This role focuses on the energy transition, sustainability, and interdisciplinary collaboration. The successful candidate will have deep knowledge of supervised and unsupervised machine learning, deep learning, optimisation, and energy data modelling.

Responsibilities include teaching core modules in MSc and BSc programmes such as Sustainable Built Environments, Energy and Resources BSc and MEng, and Energy Systems and Data Analytics. The role involves contributing to institutional leadership as Deputy Director of the MSc ESDA programme, supervising undergraduate and postgraduate students, acting as a personal tutor, and developing research-led learning and teaching strategies aligned with UCL’s Connected Curriculum. The candidate will also set, mark, and assess coursework and examinations, provide feedback, and develop a strong research portfolio.

This permanent position is available from 1 July 2026. UCL welcomes international applicants and can sponsor visas subject to eligibility.

Experience
- Strong expertise in advanced data analytics, machine learning, and AI applications in the energy sector.
- Substantial research experience in machine learning for energy, transport, or buildings, including time-series forecasting, reinforcement learning for smart grid optimisation, and causal inference for policy evaluation.
- Experience in research-led teaching focused on data analytics and advanced machine learning methods.

About you
- PhD in a quantitative discipline with a thesis directly relevant to the energy transition and the use of supervised, unsupervised, and advanced deep learning methods.
- Ability to contribute to interdisciplinary collaboration and institutional leadership.
- Commitment to developing innovative teaching and research strategies.

Qualifications
- PhD or equivalent in a relevant quantitative discipline.
- Demonstrated expertise in machine learning, AI, and energy data modelling.