PhD programme in Machine Learning Systems
First seen by Alion on Oct 9, 2026.
About the CDT
Machine Learning has a dramatic impact on our daily lives built on the back of improved computer systems. Systems research and ML research are symbiotic. Modern systems research targets the ubiquitous need for efficient ML. ML research, conversely, is directly affected by how methods will be deployed. Furthermore, systems research increasingly explores ML methods to improve systems, and ML research develops such methods.
Major gains are made when the development of ML and systems are symbiotic and co-optimized. This is relevant across a broad spectrum of industries: in-car systems, medical devices, phones, sensor networks, condition monitoring systems, high-performance compute, and high-frequency trading.
This CDT develops researchers with expertise across the systems-ML stack. This makes a cohort-based programme vital, treating ML Systems as a holistic discipline. Cohort interaction, and integration, give students real experience across multiple systems, approaches and methodologies. Company engagement is an integral part of the programme with built-in internships alongside entrepreneurship training.
The PhD programme in Machine Learning Systems positions students for strong, ethically aware technical careers, developing the next generation of leaders. Students will develop foundational research skills in Computer Systems, Machine Learning, Hardware, Sensors and Control, Programming and Integrated Machine Learning Environments, AI Ethics, and Leadership and Entrepreneurship. At the end, all students will have extensive experience of real-world deployment and optimization of machine learning methods.
Candidate’s profile
An ideal candidate would typically have:
- a strong degree or higher qualification in a relevant field (e.g. computer science, mathematics, engineering, physical sciences, economics or any other field where evidence is provided of sufficient computing and mathematical background)
- solid experience of programming, machine learning methods and ideally deep learning environments (e.g. pytorch) or a computer systems background
CDT studentships
- The CDT has a minimum of 10 fully-funded studentships available for September 2026 entry.
- Studentships include stipend, fees and research costs for 4 years.
- Home students will receive an enhanced stipend thanks to the Government TechFirst Scheme, in return of some additional requirements around widening participation and wider engagement with the TechFirst programme.
- As a UKRI-funded CDT, application is open to all UK/EU/non-EU citizens providing they meet the University PhD entry requirements.
- However, the number of students with international fees status that can be recruited is restricted (about 3 per year).
An indication of the fees status categories, entry requirements and further details on eligibility criteria can be found on our website.
Why Edinburgh?
The University of Edinburgh is one of the best research-intense Universities in the world.
The School of Informatics that hosts the CDT is consistently ranked among the world’s top 30 computer science departments. In 2026 QS World University Ranking by Subject, it is ranked 22nd for Computer Science and Information System, and 30th for Data Science and AI.
The CDT students will be hosted in the state-of-the-art Informatics Forum in the heart of Scotland’s capital, consistently considered one of the most desirable places to live in the world.
How to apply
The deadline for first-stage applications is 14th December 2026, 23:59.
We accept applications from Home applicants until 15th March 2027, pending studentships availability.
Candidates should refer to the CDT website for more information on how to apply: Apply | CDT in Machine Learning Systems | School of Informatics
Contact: [email protected]
Stipend, fees and research costs for 4 years.