Available Master topics: Scientific Computing

Network science has given us fundamental new insights into the structure of complex networks such as online social networks. Our goal is to expand these techniques to analyze social networks while they grow.
Enable high-level programming for Graphcore’s IPU Machine Learning hardware accelerator. The goal is to enable high-level Julialang-based IPU development without forcing a user to handcraft assembly or C++.
Unravel the effect of SK channel and its pharmacological block in health and atrial fibrillation, using computational models at different levels of the heart.
This master project will investigate novel use of programmable and re-configurable network adaptors for efficiently enabling data consistency which is vital for the performance of modern distributed data storage.
The Graphcore IPU is a novel AI processor that is radically different from traditional CPUs and GPUs. It offers very high memory performance, but using it requires a redesign of existing algorithms, which is topic of this thesis.