Study Centre for Doctoral Training in Machine Learning Systems PhD with Integrated Study at The University of Edinburgh in the UK, University of Edinburgh

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

UK

10

What will I learn?

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 systemsmedical devicesmobile phonessensor networkscondition monitoring systemshigh-performance computingthe creative industriespatient caresocial networkinghigh-frequency tradingHowever, PhD training that combines systems and ML is rare, as research training is often separated into individual subdisciplines.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.

Which department am I in?

University of Edinburgh

Study options

Full Time (4 Years)

Tuition fees
£34,800.00 (US$ 44,942) per year
This is a provisional fee yet to be approved
Start date

September 2027

Venue

Main Campus

Old College, South Bridge,

Edinburgh,

EH8 9YL, Scotland, United Kingdom

Full Time (4 Years)

Tuition fees
£34,800.00 (US$ 44,942) per year
This is a fixed fee
Application deadline

Expected December 2026

Start date

September 2026

Venue

Main Campus

Old College, South Bridge,

Edinburgh,

EH8 9YL, Scotland, United Kingdom

Entry requirements

For international students

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.

*There may be different IELTS requirements depending on your chosen course.

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