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Dr J Knowles
Applications accepted all year round
Competition Funded PhD Project (Students Worldwide)
About the Project
Adversarial analysis plays an important role in understanding how online algorithms (such as caching algorithms) will perform in the worst case. In multiobjective optimization, the idea has been used in some analysis of archiving strategies. The aim of this PhD would be to take this idea much further in the space of evolutionary algorithms for multiobjective optimization. In particular, how could adversaries be used to understand online decision-making and preference elicitation methods? How could they be used to understand what could happen when parts of a problem are free to change dynamically?
PhD candidates with an interest in machine learning and optimization should apply.
PhD candidates with an interest in machine learning and optimization should apply.
Funding Notes
This School has two PhD programmes: the Centre for Doctoral Training (CDT) 4-year programme and a conventional 3-year PhD programme.
School and University funding is available for both programmes on a competitive basis.
For further details, please see our funding pages here: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/
School and University funding is available for both programmes on a competitive basis.
For further details, please see our funding pages here: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/
References
The minimum requirements to get a place in our PhD programme are available from:
http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/apply/entry/
http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/apply/entry/
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