CDT in Machine Learning Systems fully-funded PhD programme
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 2027 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: mlsystems-enquiries@inf.ed.ac.uk
Website: CDT in Machine Learning Systems |
School of Informatics
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 2027 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: mlsystems-enquiries@inf.ed.ac.uk
Website: CDT in Machine Learning Systems |
School of Informatics
