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PH.D. in?Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances (Manchester-Melbourne Dual Award)

Many critical problems in logistics, manufacturing, healthcare and other fields are solved by optimisation and machine learning algorithms. Thanks to advances in automatic configuration tools, we are able to automatically tune the parameters of these algorithms for new problems with minimal human effort. Unfortunately, these tools are designed to tune algorithms according to a single criteria and assume that the characteristics of a problem do not change over time. In the real-world, however, the users of such algorithms often face conflicting criteria, such as the time required to solve the problem versus the expected quality of the solution returned by the algorithm. Moreover, it is often the case that similar problems must be solved regularly (e.g. daily) such as in the case of a parcel delivery service, a manufacturing plant processing orders in daily batches or the daily planning of operating theatres in hospitals. In those cases, the characteristics of the daily instances of the problem may evolve over time due to economical, societal and technological changes.

This project aims to extend the capabilities of automatic configuration tools to handle multiple conflicting criteria and adapt to such changes in the problem characteristics. For this purpose, the teams at Manchester and Melbourne will join their expertise in automatic configuration of algorithms and instance space Analysis.

The result of this project will be more powerful tools for tuning and deploying the critical algorithms that the modern world relies on, so that they can better adapt to changes in the problems being solved and let users decide the most Appropriate trade-off among conflicting criteria.

工作要求

The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments. A/Prof Manuel López-Ibá?ez (PI) is a worldwide expert on the automated configuration of algorithms and Prof Julia Handl is an expert in multi-objective optimisation and machine learning, including for streaming data. The team led by Melbourne Laureate Prof Kate Smith-Miles, will contribute by extending unique tools for Instance Space Analysis to Analyse and visualise the relationship between instance features and configuration criteria as well as how a stream of instances evolves over time.

This PhD project will be based at the University of Manchester with a minimum 12-month stay at the University of Melbourne.

The candidate will be enrolled in the PhD program at the Alliance Manchester Business School at the University of Manchester and in the PhD program at the School of Mathematics and Statistics at the University of Melbourne.

合同薪水

The dual-award PhD programme offers candidates the opportunity to Apply for a defined project with a strong supervisory team both in Manchester and in Melbourne. The position is four years and is fully funded, and is open to Home and International candidates. This project will be based at The University of Manchester with a 12-month stay at the University of Melbourne.

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截止時間

Application Deadline: 13?July?2022

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