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Machine Learning; Data Mining

Cardiff School of Mathematics

About the Project

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.

Unusual behaviour detection and deep learning methods can have much wider applications in other areas like target detection, intruder detection, suspicious activities monitoring etc.

The student will learn 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.
S/he also will develop an experience in mathematical programming and software development.
The student will learn 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 the student tremendously.
After this PhD, he/she will be well prepared for successful work in industrial and/or academic Mathematics


Applicants should submit an application for postgraduate study via the Cardiff University webpages:

In the research proposal section of your application, please specify the project title and supervisors of this project and copy the project description in the text box provided. In the funding section, please select the ’self -funding’ option.

We are interested in pursuing this project and welcome applications if you are self-funded or have funding from other sources, including government sponsorships or your employer.

If are applying for more than one Cardiff University project please note this in the research proposal section.

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