Postgraduate study
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Centre for Doctoral Training in Machine Learning Systems PhD with Integrated Study

Awards: PhD with Integrated Study

Study modes: Full-time

Placements/internships

Machine Learning (ML) has a great impact on our daily lives. Developments in ML are built on improved systems that can train and generate increasingly powerful models. Systems design greatly impacts ML performance and capability.

Major advancements are made when ML and systems are developed and optimised together. This is relevant across many industries such as:

  • in-car systems

  • medical devices

  • mobile phones

  • sensor networks

  • condition monitoring systems

  • high-performance computing

  • the creative industries

  • patient care

  • social networking

  • high-frequency trading

However, PhD training that combines systems and ML is rare as research training is often separated into individual sub-disciplines.

Instead, we need researchers trained in both fields and experienced in working across them. This ML Systems PhD involves training collaborative researchers with experience across systems and ML.

The programme is about machine learning that works to deliver for a need. It involves a holistic view of machine learning and systems that includes both a user-centric approach and an understanding of how to make things work.

The programme is a 4-year PhD with integrated study where you will take 180 credits of courses over years 1-3, while carrying out your PhD project research. As part of your studies, you will do an internship either in a company or the public sector (usually for 3-6 months) or an alternative form of engagement.

In the first year, you will take courses on Machine Learning Systems, Machine Learning Practical and Controversies in a Data Society. There will also be an introductory research project which will form the basis of your PhD project.

The programme is flexible to accommodate students from varying backgrounds, and the final programme of study will be agreed between the student, supervisors, and Doctoral Programme organisers.

The learning objectives for this PhD programme are:

  • world-leading research in an area of ML Systems and distributing that research through methods such as publication

  • develop expertise in an area of ML-Systems with an understanding of the full ML-Systems stack

  • experience of interacting with researchers from other areas of expertise

  • knowledge of different research environments in academia, companies and the public sector

  • deep understanding of the ethical, societal and international issues on the use and deployment of ML methods

  • skills in communicating to technical and non-technical audiences

  • active involvement in knowledge transfer and public engagement

  • organisation and leadership skills and experience

You will usually do an internship as part of the programme, but alternatives to company internships can be arranged if you prefer.

You will be supported in your study by:

  • two supervisors

  • a team of researchers associated with the research group

  • peer interaction and learning opportunities

  • training delivered by Edinburgh staff and invited lecturers

  • opportunities for entrepreneurship training

  • outreach and public communication training

  • dedicated administrative staff for the programme

You will be part of the vibrant world-class and interdisciplinary research community in the Informatics Forum and Bayes Centre. This will give you access to state-of-the-art computational infrastructure through the School of Informatics and EPCC including large GPU cluster computing in the EIDF.

Business analysts predict AI-enhanced consumer products will be the highest contributor to UK economic gains in the next decade. Therefore, there is a growing demand for PhD graduates in this area to lead this innovation. This is evidenced by the rapid growth in starting salaries and the increasing distinction between Data Scientists and ML Systems Engineers.

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

A UK 2:1 honours degree, or its international equivalent, in an area relevant to the CDT, for example, informatics, computer science, AI, cognitive science, mathematics, physics, engineering, or in another field with sufficient additional evidence of capability in the required areas.

International qualifications

Check whether your international qualifications meet our general entry requirements:

English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

  • IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
  • TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • C1 Advanced (CAE) / C2 Proficiency (CPE): total 176 with at least 169 in each component.
  • Trinity ISE: ISE II with distinctions in all four components.
  • PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS, TOEFL, Trinity ISE or PTE, in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

Academic Technology Approval Scheme

If you are not an EU, EEA or Swiss national, you may need an Academic Technology Approval Scheme clearance certificate in order to study this programme.

AwardTitleDurationStudy mode
PhD with Integrated StudyPhD in Machine Learning Systems4 YearsFull-timeTuition fees

Search for scholarships and funding opportunities:

  • Doctoral Training in Machine Learning Systems
  • School of Informatics
  • 10 Crichton Street
  • Central Campus
  • Edinburgh
  • EH8 9AB

Applicants requiring an ATAS certificate should apply by 7 April.

All other applicants are encouraged to apply as soon as they are able as we will assess applications as they arrive. Ideally applications should be submitted by 31 May 2024, but we may assess applications received beyond that point if there remains unallocated funding.

You must submit two references with your application.

You must submit an application via the EUCLID application portal and provide the required information and documentation.

This will include submission of:

  • a Curriculum Vitae (CV)
  • a research proposal (2-3 pages long)
  • degree certificates and official transcripts of all completed and in-progress degrees (plus certified translations if academic documents are not issued in English).
  • two academic references

Only complete applications will progress forward to the academic selection stage.

Read through detailed guidance on how to apply for a PGR programme in the School of Informatics:

Find out more about the general application process for postgraduate programmes:

Further information

  • Doctoral Training in Machine Learning Systems
  • School of Informatics
  • 10 Crichton Street
  • Central Campus
  • Edinburgh
  • EH8 9AB