University of Oxford

Overview

The Modern Statistics and Statistical Machine Learning (StatML) DPhil at the University of Oxford is an innovative four-year research degree aimed at developing cutting-edge statistical methods and theories that have real-world applications. This program is part of the EPSRC Centre for Doctoral Training (CDT) in collaboration with Imperial College London, providing a comprehensive educational experience in modern statistical techniques.

What you'll study

The DPhil program is primarily research-focused, supported by a structured training curriculum delivered through the StatML CDT. Students engage in original research under expert supervision, culminating in a doctoral thesis. The curriculum includes core training modules that cover essential areas in the field of statistics and machine learning.

Core training (typical areas)

• Bayesian Modelling and Computation
• Statistical Machine Learning
• Modern Statistical Theory

Research component

• Conduct original research with guidance from supervisors, leading to a doctoral thesis.
• Progress is evaluated through established milestones, aligning with Oxford's regulatory requirements for thesis submission and confirmation.

Professional development

• Training in transferable and professional skills, tailored to enhance career opportunities in both academia and industry.

Entry requirements

To be considered for the DPhil program, applicants typically need to hold at least an Upper Second Class degree or its equivalent in a relevant field. Prospective students should also meet English language proficiency requirements, which may include standardized tests such as IELTS or TOEFL iBT.

Career prospects

Graduates of the DPhil in Modern Statistics and Statistical Machine Learning are well-equipped for diverse career paths in academia, research institutions, and industry. The program’s focus on advanced statistical methodologies and machine learning prepares students for roles in data science, statistical consulting, and other data-driven fields, contributing to significant advancements across various sectors.