PhD programme in Machine Learning Systems
The University of Edinburgh
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...
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...
Opportunity details
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, mathematic...
Eligibility
Cohort interaction, and integration, give students real experience across multiple systems, approaches and methodologies. 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.
Requirements
Cohort interaction, and integration, give students real experience across multiple systems, approaches and methodologies. 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.
Funding and benefits
Funding eligibility: UK Students. Funding amount: Stipend, fees and research costs for 4 years..
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
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Recorded source check: October 9, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
Funding eligibility: UK Students. Funding amount: Stipend, fees and research costs for 4 years..
Eligibility summary
Cohort interaction, and integration, give students real experience across multiple systems, approaches and methodologies. 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.
Requirements
Cohort interaction, and integration, give students real experience across multiple systems, approaches and methodologies. 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.
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