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Research Studentship in Spatial Data Science and Zero Carbon Heat-Resilient Cities

Research Studentship in Spatial Data Science and Zero Carbon Heat-Resilient Cities

Project: Urban climate data, diagnoses, and interventions to support zero-carbon and sustainable cooling in cities.

3.5-year DPhil studentship 

Supervisors: Prof Jesus Lizana


The rapid increase of global mean temperature and unprecedented heat events require new approaches to support and monitor the climate adaptation and heat resilience of cities. Crafting effective plans necessitates accurate data and tools that adapt to the ever-changing dynamics of urban environments.

This thesis will deal with the problem of diagnosing and treating accurately, city by city, overheated urban areas (in time and space) where climate adaptation should be prioritised to promote heat resilience. The aim is to develop a groundbreaking approach to fortify climate adaptation and heat resilience in cities globally.

The thesis aims to integrate crowdsourced urban climate observations (citizen weather stations) with satellite and remote sensing data using machine learning techniques to generate high spatio-temporal resolution observations of urban atmospheric states and dynamics. The research will support the development of an urban heat diagnosis tool with global applicability to enable insight and evidence-supported actions to promote zero-carbon and sustainable cooling at different scales. The research scope will focus on three cities in different climates where data are available, and the candidate will use existing methods already developed and tested at Oxford as a starting point.

The candidate will be part of the Future of Cooling Programme and ZERO Institute at the University of Oxford. Consequently, the candidate will be supported by a multidisciplinary team of academic experts, with strong connections to policy and industry, and will be able to collaborate widely with other researchers working on climate resilience, zero-carbon energy systems, and sustainable cooling. The project will enable the candidate to build a broad skillset in spatial data science, machine learning, geospatial statistics, and cooling technologies, providing a great foundation for future careers in academia and/or industry.


This studentship is open to Home or/and Overseas students.

Award Value

Course fees are covered at the level set for Home or Overseas students. The stipend (tax-free maintenance grant) is the value of the UKRI Minimum Stipend.

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

  • A first-class honours degree (or equivalent) in any of Engineering, Computer Science, Physics, Geography, Architecture, or related fields.
  • Excellent English written and spoken communication skills.
  • Programming skills in Python.

It is desirable that candidates possess expertise or MSc degree in some of the following areas:

  • Spatial Data Science using Python
  • Remote sensing
  • Urban Climate
  • Geospatial statistics
  • Machine learning
  • Geographic Information System (GIS) software
  • Experience working in interdisciplinary teams.


Application Procedure

Informal enquiries are encouraged and should be addressed to Prof Jesus Lizana (

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website.

Please quote 24ENGCO_JL in all correspondence and in your graduate application.

Application deadline: noon on 1 March 2024 (In line with the University admissions deadline set by the University)

Start date: October 2024