Ewch i’r prif gynnwys

Machine Learning: ‘Data Mining’

This research project is in competition for funding with one or more projects available across the EPSRC Doctoral Training Partnership (DTP). Usually the projects which receive the best applicants will be awarded the funding. Find out more information about the DTP and how to apply.

Application deadline: 15 March 2019

Start date: 1 October 2019

This project will involve learning several new and important areas of IT and Mathematics: data mining, machine learning, and analysis on graphs, big data management, gpu/cuda programming and data sets fusion.

Background

Deep Learning is another name for a set of algorithms that use a neural network as an architecture. Deep learning networks simulate interconnected brain tissue and are solving many important problems.

Our main areas of research will be the interaction between mathematics and neuroscience and applications of deep learning to medical data.

Project aims and methods

You will develop an experience in mathematical programming and software development. This will involve learning how to collaborate and how to communicate results to non-mathematicians, and how to work on interdisciplinary project.

Working in these areas together with top-level researchers in the very stimulating environments of Wales Research and Diagnostic PET Imaging Centre, Cardiff University Brain Research Imaging Centre and Cardiff University School of Mathematics will benefit you tremendously.

After your PhD, you will be well prepared for successful work in industrial and/or academic Mathematics

Goruchwylwyr

Photograph of Prof Alex Balinsky

Yr Athro Alexander Balinsky

Professor of Mathematical Physics

Email:
balinskya@caerdydd.ac.uk
Telephone:
+44 (0)29 2087 5528

Gwybodaeth am y Rhaglen

I gael gwybodaeth am strwythur y rhaglen, gofynion mynediad a sut i wneud cais ewch i’r rhaglen Mathemateg.

Gweld y Rhaglen
Mae'r Academi Ddoethurol yn falch i'ch gwahodd chi i'w Gŵyl Ymchwil Ôl-raddedig cyntaf.

Rhaglenni cysylltiedig

Dolenni perthnasol