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The Distributed Algorithms PhD Programme

PhD Students, Academics and Project Partners work at the interface of high performance computing and data science to harvest the new 'oil': the information present in data.

Our Offer

Become a future leader in Distributed Algorithms and solve some of the world’s toughest challenges by working at the interface of AI, machine learning and data science. Study with a diverse team of world-leading researchers and develop flexible, fast and efficient data science solutions utilising future computing power.

How do we do it?

We achieve this through linking each PhD student to a Project Partner with a view to solving complex data challenges by co-defining where and how future algorithms will be distributed across tomorrow’s hardware. This will produce graduates who can not only develop non-trivial algorithmic solutions but understand how best to apply them to cutting-edge future computing technologies. Discover the PhD projects currently under way.

What to expect?

CDT students benefit from:

We value our students as both members of our wider community and as individuals. They are supported to meet their full potential through a fully rounded, inclusive cohort training experience. This includes training in leadership skills, experience of group and interdisciplinary team work and engagement with their wider community e.g., attendance at national and international conferences, such as FUSION and the ATI’s annual conference.

It is expected that our graduates will be employed at the forefront of meeting real-world challenges through never before seen computing capability. 

Related research

Read about our on-going research programme related to Distributed Algorithms, Big Hypotheses in our Research Section.


Andy Peace, ARA says...

Our business provides aerodynamic solutions, in particular wind-tunnel and CFD generated data.  The future means we must do things differently as our data from these sources are increasing more and more. We have a big data problem in supplying data from separate sources and then moving towards model-based design paradigms.  We want aerodynamic data models that fuse the data sets together. In order to achieve robust designs, we need to move from a deterministic world to a non-deterministic one which incorporates uncertainty into our work. We hope to achieve this by working with the CDT community to look for new ways to continue our research into these areas.

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