PhD study

EPCC offers a unique setting for in-depth research and excellent connections into industry to provide further opportunities for our students and graduates. As one of Europe's leading supercomputing centres, we host a wide, varied, and interesting hardware ecosystem. 

PhD opportunities

EPCC offers the opportunity to study for a PhD in areas related to High Performance Computing (HPC), Computational Science, Data Engineering, Data Science, and Software Sustainability.

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Woman wearing headphones taking notes beside laptop.

Meet our PhD students

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Jakub Adamski with poster describing his PhD research

PhD stories

Updates related to PhD candidates and research at EPCC.

EPCC PhD research: Compiling HPC codes for novel hardware architectures

Jake Davies is investigating whether existing programming models can run on custom hardware architectures performantly, attaining high performance and code portability across accelerators.

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Diagram of Tenstorrent Blackhole

Duolingo dissertation grant supports research into AI-generated mathematical stories

A Duolingo dissertation grant has been awarded to a team at the University of Edinburgh to support research into AI-generated mathematical storytelling.

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MathsTale logo with graphic of open book

What's it like to study for a PhD at EPCC?

Jake Davies' research is focused on compiling high-performance codes for RISC-V-based novel architectures. Here he writes about his experience as a PhD student at EPCC.

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Jake Davies with computer hardware

From PhD candidate to EPCC team member

Shrey Bhardwaj became an applications developer at EPCC last month, after having first joined us as a PhD student.

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Shrey Bhardwaj

Improving data flows for weather forecasting

EPCC PhD student Nicolau Manubens, in collaboration with supervisor Adrian Jackson and the European Centre for Medium-Range Weather Forecasts (ECMWF), presented work on utilising object storag

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Colourful umbrellas by Engin Akyurt at Pexel

PhD research: enhancing Sparse Linear Algebra with AI and HPC

In the rapidly evolving landscape of high-performance computing, the challenge of efficiently processing large and sparse datasets is a critical hurdle across various scientific and engineering dom

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Fig 1 described in article.