Fully Funded PhD Scholarship Aberystwyth University, UK

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A fully-funded PhD scholarship for the period of 4-year in the UK. Centre for Doctoral Training in research areas of:

Artificial Intelligence

Machine Learning & Advanced Computing System

Machine Learning Environment Adaptive Multimodal Vision System

fully funded PhD scholarship Aberystwyth University, UK

Project/ Award Title

Few-shot Learning for Environment Adaptive Multimodal Vision System

As a dominating technique in AI, deep learning has been successfully wanted to facilitate a mess of visual tasks, like recognizing faces, tracking emotions, or monitoring physical activities. However, each of those tasks requires training a neural network on a really large image dataset specifically collected and annotated for that task. Though the trained networks are experts for the target task, they only understand the ‘world’ experienced during training and may ‘say’ nothing about other content, nor can they be adaptive to other tasks without retraining. Moreover, most visual algorithms are learning from ‘single modal’, but pay no attention to other vision modalities, like depth and thermal sensors.

The core objective of the project is to develop a subsequent generation of machine learning algorithms that will mimic human vision and intelligence – continually learning to adapt to the new environment from a couple of visual shots without requiring the normal ‘strong supervision’ of a replacement dataset of every new task. Compared to the traditional supervised setting, learning from few shots poses several challenges thanks to, e. g. insufficient training data during a new environment; the bias between old and new tasks; the continually emerging tasks; the catastrophic forgetting problem when learning new tasks.

Reference Material

[1] L. Xiang, G. Ding and J. Han, Learning from Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification, in proceeding of European Conference on Computer Vision (ECCV 2020), spotlight paper (top 5%).

[2] D. Zhang, H. Tian and J. Han, Few-Cost Salient Object Detection with Adversarial-Paced Learning, in proceeding of 34th Conference on Neural Information Processing Systems (NeurIPS 2020).

Eligibility and Student background

We are seeking an enthusiastic individual to join the Computer Science Department at Aberystwyth University, UK, with the following attributes:

A minimum 2:1 undergraduate (BEng, MEng) and/or postgraduate masters’ qualification (MSc) in a science and technology field: Computer Science, Engineering, Mathematics, with specialization in Computer Vision, Machine Learning and AI

Appropriate IELTS score (overall score of 6.0 with no component below 5.5) or TOEFL.

Familiarity with machine learning and probabilistic models

Relevant software knowledge and experience, for example, Python and tensor frameworks (PyTorch or TensorFlow), C++, etc

A driven, professional and independent work attitude

Excellent written and verbal communication skills

Funding Notes

The 4-year PhD scholarship will sit within the UKRI Centre for Doctoral Training in Artificial. Intelligence, Machine Learning & Advanced Computing

Funding will cover the full cost of tuition fees and an annual stipend of £15,285 for 4 years

The post is open to both Home/EU and overseas students

Additional funding is available for training, research and conference expenses

How to Apply

Applications through Aberystwyth’s electronic application process Aberystwyth University- Postgraduate Study: How to apply must include the following attachments in pdf form

1. CV

2. Degree certificates and transcripts (if you are still an undergraduate, provide a transcript of results known to date)

3. A statement no longer than 1000 words that explain why you want to join our Centre, and your preferred topic/supervisor.

4. Academic references-all scholarship applications require two supporting references to be submitted. Please ensure that your chosen referees are aware of the funding deadline (to be determined), as their references form a vital part of the evaluation process. Please include these with your scholarship application.

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