Phil Symonds

University College London (UCL) is offering a fully funded PhD opportunity for Home applicants at the Bartlett School of Environment, Energy and Resources. The project applies Bayesian statistics and machine learning to fuel poverty and net-zero housing. The research will develop innovative methods to identify retrofit need at the building level using large-scale housing data. It will combine machine learning, building simulation and spatial Bayesian modelling to support evidence-based housing retrofit and decarbonisation decisions. The PhD will be supervised by Phil Symonds, Giorgos Petrou and Zaid Chalabi. Applicants should note that the post explicitly limits eligibility to Home applicants. Funding is provided through the UCL EPSRC Doctoral Landscape Award (UELA) Studentships and is described as fully funded, although no stipend amount or detailed fee coverage is stated. Interested candidates should consult the UCL studentship information and submit an application through the provided link.