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Artificial Intelligence and Machine Learning for Biomedical Data

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Artificial Intelligence (AI) and Machine Learning (ML) are the leading edge approaches to data driven problems across all areas of life, technology and sciences. This project aims to develop novel AI/ML techniques with focus on Deep Learning Neural Networks, aspects of controlling model complexity and understanding better the data distribution factorization produced by such neural networks. The methods developed in this project will be applied to analyze biomedical data (e.g. bone cancer xray imaging data).

The PhD project is expected to lead to a series of publications in leading conference proceedings and scientific journals.

Keele University is renowned for its exciting approach to higher education and research, beautiful campus, strong community spirit and excellent student life. The University has the UK’s largest campus with 617 acres of landscaped parkland, fields, woodlands and lakes. Keele University runs its own day nursery for infants from 3 months to 5 years and is committed to equality and diversity. Information for prospective postgraduate researchers can be found here: http://www.keele.ac.uk/pgresearch/

Research Context: This PhD project will connect with on-going collaborative research activities in areas of development and application of machine learning and artificial intelligence methods in the context of biomedical data analysis. The research will be supervised by Professor Peter Andras in the Centre for Computer Science Research at Keele University. The project may include collaboration with biomedical researchers at Keele University or other universities.

Informal enquiries about the project are very welcome by email to the Project Lead, Professor Peter Andras (). Full applications should be submitted via: https://www.keele.ac.uk/study/postgraduateresearch/researchareas/computerscience/

Applications are welcomed from science, technology, engineering or mathematics graduates with (or anticipating) at least a 2.1 honours degree or equivalent. Applicants will require good general computing skills and general understanding of mathematics, but will not require in-depth understanding of machine learning.

Applicants should have an enthusiasm for analysis and development of algorithms and experimentation as well as a willingness to acquire new skills. Ideally, applicants will be self-motivated and have the ability to work both independently and as part of a team.

This opportunity is open to UK/EU and overseas students. The collaborative and presentation aspects of the research require good English language and communication skills. Overseas applicants would therefore require an English IELTS (or equivalent) of 6.0 overall with no less than 5.5 in any subtest.

Applicants should be self-motivated and enjoy working both independently and as part of a team.

Please go to https://www.keele.ac.uk/study/postgraduateresearch/apply/ for information about application.
Please quote FNS GS 2019-28 on your application.

Keele University values diversity, and is committed to ensuring equality of opportunity. In support of these commitments, Keele University particularly welcomes applications from women and from individuals of black and ethnic minority backgrounds for this post. The School of Computing and Mathemtics and Keele University have both been awarded Athena Swan awards and Keele University is a member of the Disability Confident scheme. More information is available on these web pages:
https://www.keele.ac.uk/equalitydiversity/
https://www.keele.ac.uk/athenaswan/
https://www.keele.ac.uk/raceequalitycharter/raceequalitycharter/

Funding Notes

Open to fully self-funded students only.
Please note that self-funded applicants must provide funding for both tuition fees and living expenses for the 3 year duration of the research. There is a future possibility of competitive scholarship awards for outstanding applicants (1st class honours), however, none are currently available.
For information regarding University tuition fees please see: View Website

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