You will be working under the guidance of Professor Richard Wilson in the area of Geometric Deep Learning. Geometric deep learning is the generalisation of ideas from deep neural networks to problems on graphs, networks and meshes. Examples include drug design, shape modelling, protein interactions and understanding social networks. The goal of the project is to develop new algorithms for deep learning with this kind of data.
The work will have two main themes; the application of spectral methods to deep learning for graphs, and generative models which can provide new samples from a dataset. This area has been little explored by the machine learning community so far. We aim to combine ideas from graph convolutional networks, spectral graph theory and graph representation to build more effective algorithms with richer features.
The ideal candidate for the role will have a solid background in programming and mathematics, and experience of using deep learning on large datasets. The work will involve research skills, problem solving and technical ability in implementation.
(Role holders will be required to undertake some or all of the duties below)
Interview date: To be confirmed
For informal enquiries: please contact Richard Wilson (firstname.lastname@example.org)
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