Intelligent Earth (UKRI CDT in AI for the Environment) PhD

**The information provided on this page was correct at the time of publication (November 2024). For complete and up-to-date information about this course, please visit the relevant University of Oxford course page via www.graduate.ox.ac.uk/ucas.**<br/><br/>The Intelligent Earth CDT is a four-year doctoral programme designed to equip a new generation of students with advanced AI skills to tackle some of the most pressing environmental issues. <br/><br/>The programme will train a new generation of quantitative environmental data scientists to make substantial contributions in environmental and data sciences through five closely connected themes:<br/><br/><br/>- Climate<br/><br/><br/>- Biodiversity<br/><br/><br/>- Natural hazards<br/><br/><br/>- Environmental solutions<br/><br/><br/>- Core AI/ML research on complex environmental data<br/><br/><br/>The programme is intrinsically interdisciplinary: you will be advised by both an environmental science supervisor and an AI supervisor from two different departments, plus a non-academic partner who also serves as host for a secondment. This course is suitable for quantitative applicants from data science, mathematical, physical and environmental science backgrounds. <br/><br/>The teaching model for all courses will be tailored towards training students to become independent researchers. After introductory lectures, you will be introduced to the corresponding AI tools, frameworks and environmental datasets to apply the taught material in tutorial-based project work. You will work in interdisciplinary groups tackling grand challenges in environmental science of increasing complexity with AI. The programme will be individually tailored to your needs.<br/><br/>Key components of the teaching programme:<br/><br/><br/>- Induction week<br/><br/><br/>- Core courses in foundations of AI/ML and foundations of the four environmental themes<br/><br/><br/>- Responsible AI training<br/><br/><br/>- Computational skills training<br/><br/><br/>- Advanced cross-cohort courses will focus on specific areas of AI applied to grand challenges and associated datasets from the four environmental themes<br/><br/><br/>- Professional skills training<br/><br/><br/>- Teaching skills training<br/><br/><br/>- <br/><br/><br/>In the second half of year one, you will undertake a three-month research project supervised by one of the potential DPhil supervisors.<br/><br/>In addition to the formal teaching programme, student experience and training will be enriched by:<br/><br/><br/>- Weekly Intelligent Earth seminars<br/><br/><br/>- Annual two-week hackathons<br/><br/><br/>- Annual two-day CDT conference<br/><br/><br/>**Course structure**<br/>In year one, you will take core courses and computational skills training courses, followed by advanced cross-cohort courses, responsible AI training, and professional skills training modules, culminating in a three-month research project followed by the annual hackathon and conference. Course free periods will be used for consolidation, supervisor matching, and DPhil proposal development.<br/><br/>In year two, you will transition to your primary department and supervisors, and you will start your DPhil research. You will take advanced cross-cohort courses and professional/computational skill training modules. A secondment with non-academic partners may also take place at this stage, but may alternatively take place in year three.<br/><br/>In year three, your focus will be on DPhil research with optional advanced courses and professional/computational skill training modules based on your individual training needs. A secondment with non-academic partners may also take place at this stage if it was not undertaken in year two.<br/><br/>In year four, you will finalise your DPhil research and complete your thesis writing. Professional training will focus on career development, job/fellowship applications and interviews.