PhD Study : ​​AI-Driven Approaches to Enhance Human Activity Recognition​

Summary

Interested in pushing the boundaries of Artificial Intelligence (AI) and Machine Learning to understand human activities through sensor data? We invite you to apply for this funded PhD position, where you'll play a vital role in advancing the state-of-the-art in sensor-based Human Activity Recognition (HAR).

AI, particularly machine learning, has transformed our ability to interpret human activities using data from wearable, mobile and ambient sensors. Nevertheless, the field of sensor-based HAR has faced challenges due to the limited availability of large-scale, high-quality labeled datasets, hindering the development of robust machine learning approaches.

As a successful candidate, you will lead investigations into computational approaches aimed at facilitating the development of AI solutiongs capable of recognising a wide array of human activities. Your research will delve into advanced machine learning and AI techniques, enabling the recognition of a diverse set of activities from various sensor modalities. Additionally, you will pioneer methods to reduce the effort required for acquiring annotated training data.

Research Challenges: Depending on your interests, you can focus on a range of compelling research challenges:

1. Data Labelling Assistance:
   • Innovate approaches to streamline the labelling process for HAR data.
   • Develop models that require less labelled data (contrastive learning, self-supervised learning, and self-training).
   • Create tools for automated or semi-automated labelling and active label cleaning.
   • Investigate the role of data augmentation in training.
   • Investigate Zero-shot / Few-Shots learning for HAR
2. Personalisation for Cross-Subject Variability:
   • Combine semi-population and user adaptation techniques to personalise models for diverse populations.
   • Explore the potential of transfer learning and evaluate its effectiveness in under-represented populations.
3. Approaches to Utilise Multi-Modal Data:
   • Pioneer approaches to leverage a diverse range of sensing modalities.
   • Develop methods for integrating multi-modal sensing through feature fusion and classifier ensembles.
   • Explore emerging approaches to multimodel deep learing for HAR (i.e.transformer-based architectures).

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• For VCRS Awards, Masters at 75%
• Completion of Masters at a level equivalent to commendation or distinction at Ulster
• Experience using research methods or other approaches relevant to the subject domain
• Sound understanding of subject area as evidenced by a comprehensive research proposal
• Publications - peer-reviewed

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living