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Optimisation surface characterisation

   School of Physics, Engineering and Technology

About the Project

Many projects in Engineering and Computer Science involve global multivariate optimisation. Classical methods include genetic algorithms, simulated annealing and particle filters. One way to think about global optimization is as density estimation, where Markov chain Monte Carlo methods attempt to learn the properties of an optimisation surface as if it were a probability distribution. This project is about doing that surface characterisation in a new way. The idea is to begin local ascent algorithms from multiple start points and then use the information about distance and directions travelled in each case to estimate maxima density. From this, we would make principled choices about how many starts to attempt and stopping criteria for a high probability of finding the global maximum.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in Electronic and Electrical Engineering, Physics, Computer Science, Mathematics, Music Technology or a closely related subject.

How to apply:

Applicants should apply via the University’s online application system at Please read the application guidance first so that you understand the various steps in the application process.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website View Website for details about funding opportunities at York.

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