
How to apply
Apply for a joint PhD with the Toronto-Melbourne Research Training Group.
This is one of two research projects developing new tools for automated algorithm selection and configuration. The University of Manchester is the home institution for this project. To view the Melbourne-based partner project, click here.
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 now 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 criterion 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 (for example daily) 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 the 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 our 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.
The goals of this project are to:
The main research questions are:
*Click on the researcher's name above to learn more about their publication and grant successes.
We are seeking a PhD candidate with the following skills:
The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments. A/Prof Manuel López-Ibáñez (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.
The supervisory team at Manchester are both members of the Institute for Data Science and Artificial Intelligence (IDSAI) of the University of Manchester and Turing Fellows of the Alan Turing Institute. Both institutes provide access to a large network of researchers within the University of Manchester and the UK. In particular, colleagues at IDSAI working on mathematical optimisation and machine learning algorithms for manufacturing, logistics and healthcare problems will be interested in learning more about the capabilities provided by the methodology and MATILDA tool developed by Melbourne.
To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply.
Apply for a joint PhD with the Toronto-Melbourne Research Training Group.
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