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Using Machine Learning to Improve Quantifier Elimination Procedures

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  • Full or part time
    Dr M England
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Machine Learning (ML) refers to artificial intelligence techniques that employ a combination of statistics and big data to solve problems. A prominent example is the classification of images according to search terms.

A Computer Algebra Systems (CAS) is a piece of software that performs symbolic mathematical computations with exact precision, as a human mathematician would by hand. Prominent examples include Maple and the web interface Wolfram Alpha.

This project will improve the performance of a CAS using ML. It may seem that the inherently probabilistic nature of ML tools would invalidate the exact mathematical results prized by a CAS, however, mathematical algorithms often come with choices which have no effect on the mathematical correctness of the end result but a great effect on the resources required to find it. Such choices are prime candidates for ML, and successful application here could greatly improve system performance and in turn impact on the wide range of engineering and scientific applications.

The particular focus of the project is Quantifier Elimination: the process of taking a quantified logical system and finding an equivalent unquantified one. This may be thought of as simplifying the mathematical expression: making explicit those properties than were hidden before.

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College which provides support with career development activities.

The candidate for this project will work in the Centre for Data Science amongst other researchers employing similar data science techniques to a wide variety of problems and applications. With access to cutting edge techniques and high performance computing resources, the Centre for Data Science is an ideal place to pursue a PhD in Machine Learning.

Entry criteria for applicants to PhD

A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
PLUS
• the potential to engage in innovative research and to complete the PhD within a 3.5 years
• a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)

• The relevant discipline specified in the general requirements above is here either Mathematics or Computer Science.
• If Mathematics then the candidate is expected to have also gained demonstrable experience in programming.
• If Computer Science then the candidate is expected to have Mathematics A-Level (or equivalent) and excellent scores in the mathematics based modules of their Computer Science degree.

• Experience conducting scientific computational experiments, literature review, and software development are all advantageous.

For further details see: https://www.coventry.ac.uk/research/research-students/making-an-application/

How to apply

Apply on line https://pgrplus.coventry.ac.uk/ submitting full supporting documentation, and covering letter only

Eligibility: UK/EU/International students with the required entry requirements

Funding award: Bursary plus tuition fees - UK/EU/International

Duration of study: Full-Time – between three and three and a half years fixed term

Application deadline: Sunday 29th March 2020

Interview dates: Week beginning Monday 27th April 2020.

Start date: September 2020

Funding Notes

This PhD position is fully funded to include tuition fees and stipend.



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