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PhD Studentship: Machine Learning from Time Series

University of Southampton

Qualification Type: PhD
Location: Southampton
Funding for: UK Students, EU Students, International Students
Funding amount: Funding for tuition fees and a living stipend are available on a competitive basis. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
Hours: Full Time
Placed On: 30th November 2023
Closes: 31st August 2024
 

Supervisory Team:    Anthony Bagnall and Matthew Middlehurst

Project description

Time series problems arise in all areas of scientific enquiry. For example, human activity recognition from motion traces and diagnosis from medical signals such as EEG/ECG all involve data observed over time. Time series machine learning (TSML) [1,2,3,4,5] offers the potential to contributing to a huge range of fields to give fresh insights into important applications.

TSML has specific challenges not found in traditional machine learning and requires bespoke learning algorithm to exploit the ordered nature of the data [1,5]. The aims of this project are to improve existing algorithms [2,3,4] to make them more scalable; to develop novel deep learning and ensemble approaches for the learning tasks; to improve the useability and explainability of the methods; and to apply them to case studies with our scientific and industrial collaborators such as Gt. Ormond St. Hospital.

The successful candidate will join a vibrant and active research group. We all work with the same codebase, the aeon toolkit and have a consistent track record of integrating new members. We also collaborate extensively with international partners, and there will be scope for collaborations with and visit to researchers in, for example, US, Australia, Brazil, France, Spain and Germany.

Note that allocation of PhD funding happens every one or two months, and once funded this position will close, so do not delay in applying.

References:

[1] A. Bagnall et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances, Data Mining and Knowledge Discovery, 31, 2017

[2] C. Holder et al. “A Review and Evaluation of Elastic Distance Functions for Time Series Clustering” Knowledge and Information Systems, in press, 2023.

[3] M. Middlehurst et al. “HIVE-COTE 2.0: A New Meta Ensemble for Time Series Classification.” Machine Learning 110: 3211–43, 2021.

[4] D. Guijo-Rubio et. Al. “Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression”, arXiv:2305.01429, 2023

[5]  M. Middlehurst et al. “Bake off redux: a review and experimental evaluation of recent time series classification algorithms”, arXiv:2304.13029, 2023

If you wish to discuss any details of the project informally, please contact Tony Bagnall, VLC Research Group, Email: a.j.bagnall@soton.ac.uk, Tel: +44 (0) 2380 59 26894.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 August 2024 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: Funding for tuition fees and a living stipend are available on a competitive basis. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Elect & Elect Eng (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Anthony Bagnall

Applications should include:

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk

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