Our programme
Our Programme
StatML is a fully funded, cohort-based doctoral training programme, jointly offered by Imperial College London and the University of Oxford. This four-year integrated PhD (Imperial) / DPhil (Oxford) (or longer if studying part-time) prepares the next generation of researchers in statistics and statistical machine learning.
Students engage in cutting-edge research, develop innovative methodologies and theoretical frameworks, and create real-world applications that drive advancements in government, healthcare, industry, and science. As part of a dynamic, collaborative cohort, they benefit from a strong alumni network and interdisciplinary learning environment.
Beyond research, the StatML Advanced Training programme offers specialized modules in statistics and machine learning, computational skills training, hands-on data dives, guest lectures, and professional development. This comprehensive training equips graduates with expertise highly sought after in both academia and industry.
Research at StatML
StatML provides a dynamic research environment that allows students to explore and refine their academic interests before fully committing to a PhD project.
In the first year, students undertake two 10-week mini-projects. These projects offer exposure to cutting-edge research topics, opportunities to work with different supervisors and research groups, and the chance to develop essential research, academic writing, and presentation skills.
By the end of the first year, students define their PhD research direction and begin work on their long-term project, continuing throughout the remainder of the programme. Progress is guided by structured milestones at Imperial (early stage assessment at 12 months, late stage review at 24 months) and Oxford (transfer of status at 18 months, confirmation at 36 months), ensuring comprehensive feedback and academic support.
Many StatML PhD studentships involve collaboration with a specified industry partner, providing students with the opportunity to work on real-world challenges. These industrially funded PhD students benefit from co-supervision by company experts alongside Imperial and/or Oxford academics, contributing to the development of advanced statistical and machine learning methodologies for impactful applications.
StatML advanced training programme
StatML Theory & Methods
Students receive advanced training in statistics and machine learning through a structured series of four core modules in Year 1 (covering modern statistical theory, statistical machine learning, causality, and Bayesian methods). This foundation is expanded through bespoke and responsive elective modules on specialised topics taught by leading academics and industry experts.
Computing & Software Engineering
In Year 1, students complete a core computing module (covering Python, HPC, and software engineering best practices) and can later choose from advanced elective micro-courses, often led by industry partners.
Communication, Public Engagement & Outreach
Students receive specialised training in communication and public engagement, with numerous real-world opportunities to hone their ability to convey complex ideas to diverse audiences effectively.
Responsible research and innovation
Students engage in cutting-edge discussions on the ethical and societal implications of AI and ML through a core RRI module.
Industry Engagement & Real-World Data
Students gain hands-on experience with complex, real-world datasets through industry-led “data-dive” events, where they tackle practical challenges alongside experts. Additionally, internships are strongly encouraged, providing an opportunity to apply research skills in professional settings, gain industry insights, and expand professional networks. While not mandatory, internships offer invaluable experience for students considering careers beyond academia.
International & Interdisciplinary Collaboration
Students participate in the StatML-Bocconi Summer School, an annual event alternating between the UK and Italy, featuring world-renowned lecturers and fostering international connections. They are also strongly encouraged to attend academic conferences, workshops and meetings, and undertake academic visits abroad, supported by a generous research and training grant.
Admissions
StatML Applications for 2025 are now closed!
StatML’s main admission cycle is currently closed for 2025-26. Explore our admission process, timeline and institutional policies in preparation for entry in 2026! Information for the upcoming admissions round will appear during Autumn 2025.