Welcome to StatML
A world leading centre for doctoral training shaping the next generation of researchers in statistics and machine learning.
What is StatML?
The EPSRC Centre for Doctoral Training in Statistics and Machine Learning at Imperial and Oxford (StatML) combines two of the UK’s foremost institutions in the field, with a diverse group of industrial and international partners, to shape the next generation of researchers in statistics and machine learning.
We train future researchers, diverse in their backgrounds and perspectives, to develop novel methodology and theory and create application-specific methods, leading to breakthroughs in real-world problems in science, government, medicine, and industry. Our exciting training programme empowers students with the advanced technical and practical skills required to provide real-world impact, solve critical global challenges and create society- changing technologies.


Doctoral programme
StatML is a four-year, fully funded, cohort-based doctoral training centre, with part-time study options available. Our structured programme equips students with the foundational skills and knowledge needed to conduct impactful, ethical, and responsible research. In addition to advanced training in statistics, machine learning, computing, and communication, students engage in first-year mini-projects that allow them to explore different research areas and shape their PhD journey. This is further enriched by a range of scientific and networking events. Learn more about our programme here.
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.
Our students
Discover the breadth of background and research interests of our cohorts by exploring our students’ page.
Latest news
Industry & academic partners
StatML has an extensive network of partners across a number of technology sectors and academia to support students and offer additional value to the CDT via internships, placements and studentships.