About us
The Bartlett Centre for Advanced Spatial Analysis (CASA) is globally recognised as a leading academic department, researching and teaching the science of cities. A truly interdisciplinary centre, with staff hailing from backgrounds in subjects as diverse as Geography, Transport Studies, Mathematics, Physics, Computer Science, Statistics, Planning, Architecture, the Humanities and the Social Sciences, for more than 20 years we have been combining theory with novel data, sensors, computational models, analysis and cutting edge visualisation to generate new knowledge and insights addressing problems with a spatial dimension and an urban and regional focus. Our work in inherently applied and looks to influence urban planning, policy and design in both the public and private spheres. Educating only postgraduates, our degree programmes have gained a reputation for excellence and inclusivity, with student cohorts also reflecting a hugely diverse range of academic backgrounds. Further information can be found on our website at https://www.ucl.ac.uk/bartlett/casa/
About the role
The Bartlett Centre for Advanced Spatial Analysis (CASA) is recruiting a Research Fellow in Urban AI to contribute to an Innovate UK-funded collaborative project - "Increasing EV charging capacity on the strategic road network". The project aims to develop an intelligent buffered ultra-rapid EV charging solution for grid-constrained motorway service areas. The role will focus on developing data-driven AI and spatial modelling approaches to understand and predict EV charging demand, particularly to optimise charging and energy management, and to assess the potential transferability of the solution across England's Strategic Road Network. Working within the UCL research team and in close collaboration with industry partners, the research fellow will integrate large-scale EV charging, energy, transport, grid, and geospatial data to develop and validate models at both the charging-session and site/network scales. The research will contribute to a proof-of-concept AI-driven energy management solution and provide evidence to support the future planning, operation and deployment of buffered ultra-rapid charging infrastructure. The Research Fellow in Urban AI will: undertake research and analysis using EV charging, energy, transport and geospatial data to support the objectives of the project; undertake data acquisition, processing and integration, working with EV charging, energy, mobility and geospatial datasets from multiple sources; develop and test deep reinforcement learning or alternative AI-based approaches for intelligent charging and energy management; explore innovative approaches to intelligent charging and energy management that account for user behaviour and activity patterns. The post is available from 1st January 2027 and is funded until 31st December 2027 in the first instance. Starting salary offered will be £46,261- £51,246 per annum, inclusive of London Allowance, due to limited amount of funding available. Please note: Appointment at Grade 7 is dependent upon PhD award. If not yet awarded, initial appointment will be at Grade 6B (£39,807 - £42,891 per annum, inclusive of London Allowance), with backdating to Grade 7 upon submission of PhD thesis.
This appointment is subject to UCL Terms and Conditions of Service for Research and Professional Services Staff. Please visit https://www.ucl.ac.uk/human-resources/conditions-service-research-teaching-and-professional-services staff for more information. Find out about the UCL Ways of Working: www.ucl.ac.uk/ways-of-working. We will consider applications to work on a part-time, flexible and job share basis wherever possible. For any queries about the role please contact Prof Chen Zhong (c.zhong@ucl.ac.uk). A job description and person specification can be accessed at the bottom of this page. To apply for the vacancy please click on the '
About you
The successful candidate will hold a PhD (or close to completion) in ML/DL, GeoAI, urban informatics, Spatial Data Science or a closely related quantitative discipline. They will also have: research experience in spatial modelling, machine learning or deep learning, with applications to mobility, transport, energy or related domains; experience in working with and analysing large and complex spatial datasets; knowledge of, or experience with, emerging data-driven approaches and service innovations in EV charging, including AI-enabled modelling, charging flexibility and intelligent energy-management strategies; strong technical skills in AI/ML modelling (e.g., Python), data driven modelling; ability to acquire, process, integrate and analyse large and heterogeneous datasets from multiple sources, in particular, spatiotemporal data; ability to understand and incorporate relevant energy-system and operational constraints or policy into modelling and analysis; ability to produce well-structured, documented and reproducible research outputs, including code, data-processing workflows and supporting documentation, and to prepare and maintain repositories for sharing through platforms such as GitHub; ability to work independently, identify research opportunities and develop innovative methodological approaches beyond established project specifications; ability to communicate effectively across disciplines and collaborate with researchers and stakeholders from industry. For full list of essential and desirable criteria, please see a job description and person specification at the bottom of this page.
Our commitment to Equality, Diversity and Inclusion
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 Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women. You can
