About the project
This project advances time-series machine learning, including classification, clustering, and regression, by scaling and extending state-of-the-art methods, designing novel deep and ensemble models that exploit temporal structure, and improving usability and explainability. Research is driven by real-world case studies and delivered via the open-source aeon toolkit, with extensive international collaboration.
Time series machine learning underpins applications from human activity recognition to clinical decision support. This project will push the state of the art in time-series classification, clustering, and regression by developing algorithms that exploit temporal structure more effectively. We will scale and extend the HIVE-COTE family to all three tasks, unifying elastic, feature-based, and deep representations within robust ensembles.
Methodologically, the project will:
- optimise and improve existing algorithms for performance and efficiency
- design new hybrid deep-ensemble methods that fuse components intelligently through calibrated uncertainty
- improve usability and explainability
Case studies with scientific and industrial collaborators will ensure real-world relevance, covering modalities such as wearables and sensors. You will join a vibrant research group and open-source community based around the aeon toolkit and engage in international collaborations. The exact technical emphasis will evolve with your interests.
Outcomes will be:
- faster, more accurate, and more interpretable TSML methods
- a generalised HIVE-COTE framework for classification, clustering, and regression
- open-source implementations that lower barriers to adoption
In addition to standard training opportunities there will be opportunities for short internships with international partners in Australia, Ireland, Spain, France and Germany.
The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break.
The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
