Apply now for the fully funded PhD at the University of Edinburgh’s CDT in Machine Learning Systems. Learn about eligibility, funding, and how to apply.

About the CDT in Machine Learning Systems

The Centre for Doctoral Training (CDT) in Machine Learning Systems at the University of Edinburgh offers an exceptional fully funded PhD opportunity for students passionate about machine learning, data science, and artificial intelligence research.

Hosted by the School of Informatics, the programme combines world-class academic training with strong industrial collaboration, preparing researchers to design and implement the next generation of intelligent, data-driven systems.

This PhD under the CDT in Machine Learning Systems aims to bridge the gap between algorithmic innovation and real-world deployment. Students learn to apply theoretical insights to large-scale, impactful technologies that advance both science and society.

Learn more on the official CDT in Machine Learning Systems page.

Eligibility and Entry Requirements

Before applying, candidates should ensure they meet the academic and English language requirements listed on the Eligibility and Entry Requirements page.

Applicants typically hold a first-class or upper second-class degree (or equivalent) in computer science, mathematics, engineering, or a closely related field. They should demonstrate strong research potential and genuine interest in advancing machine learning systems at scale.

Funding and Studentships

The CDT in Machine Learning Systems provides fully funded PhD studentships for eligible applicants. Funding includes:

• Full coverage of tuition fees (UK and international)

• An annual stipend for living expenses

• Access to state-of-the-art facilities, training, and research resources

Students may also apply for company-linked studentships, which include partnerships with leading technology companies for applied research projects.

Detailed information can be found on the Funding Details and Company-Linked Studentships pages.

How to Apply

Applications to the CDT in Machine Learning Systems follow a two-stage process. Candidates must first complete the online first-stage application form hosted by Jisc, providing academic details, research interests, and supervisor preferences.

Apply online: CDT MLSystems Application Form (Jisc)

Before applying, ensure you have:

• Reviewed all eligibility and funding information

• Contacted potential supervisors from the CDT Supervisors List

• Read the Application Guidance carefully

• Checked the Selection Process and Timeline

You can also download the Word/LibreOffice version of the first-stage application template directly from the Apply Now page.

Programme Overview

The PhD programme runs over four years and offers interdisciplinary training that combines coursework, research, and industrial experience. Students benefit from:

• Mentorship by leading researchers in machine learning and AI

• Hands-on collaboration with global industry partners

• Participation in seminars, conferences, and networking events

• Access to the vibrant academic community within the School of Informatics, one of Europe’s top computer science institutions

More programme details are available on the Programme page.

Application Timeline

The CDT in Machine Learning Systems operates on a rolling application process, with several deadlines each academic year.
Applicants are encouraged to apply early to maximize funding opportunities and supervisor availability.

Updated information can be found on the Application Deadlines page.

Deadline: Round 1 closes 11 December 2025

Apply here.

Conclusion

The fully funded PhD at the CDT in Machine Learning Systems represents an unparalleled opportunity for ambitious researchers to shape the future of AI and data-driven innovation.
By joining the University of Edinburgh’s world-renowned academic community, students will gain the knowledge, skills, and professional network to lead cutting-edge research in machine learning systems and beyond.

Participants will work alongside top scientists, industry partners, and interdisciplinary teams, developing solutions to real-world challenges in automation, optimization, and responsible AI. The programme not only advances technical expertise but also builds leadership and communication skills essential for careers in academia, research, and technology-driven industries worldwide.

For more international career opportunities, visit Opportunities For Youth