Bath Engineering & Design Studentships: Knowledge Representation in Bio-Inspired Neural Networks

Postgraduate Studentships; Bath; Hybrid; GBP 21,000 - 23,000; Full time

Benefits offered by this job

Tuition fees covered

Maintenance stipend

Training support fee £1k p.a.

Job summary

Bath Engineering & Design PhD Studentship focuses on knowledge representation in bio-inspired neural networks at the University of Bath. The project explores spiking neural networks, abstraction and localised model editing, with potential publications in AI and Computational Neuroscience venues.

Applicants should have a UK Honours degree 1st or 2.1 (or international equivalent); a Master’s degree is advantageous.

Qualifications

• UK Honours degree 1st or 2.1, or international equivalent, in a discipline relevant to the PhD.
• Master’s level qualification would be advantageous.

Responsibilities

• Work on knowledge representation in AI models and localised model editing.
• Publish findings in AI and Computational Neuroscience venues as appropriate.

Education

UK Honours degree 1st or 2.1, or international equivalent

Job description

Bath Engineering & Design Studentships: Knowledge Representation in Bio-Inspired Neural Networks

ThisprojectisoneofanumberincompetitionforastudentshipfromtheFacultyofEngineeringandDesignattheUniversityofBath.Ifsuccessful,thisstudentshipisexpectedtocommence04October2027.

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Artificial Intelligence (AI) currently dominates most modern computing tasks, predominantly through their scaled-up versions deep neural networks (DNNs). Despite the constant improvement in efficiency, the data and power demands of AI are ever growing due to their very fast-paced development.
This project focuses on bio-inspired knowledge representation in AI models. For example, we may want to replace an existing face profile in a facial recognition system with a different face profile without retraining the entire model. To do this efficiently, one needs to identify the specific parts of that model representing the information of interest. This challenge is closely related to abstraction and concept learning, which are central to human generalization but remain less explicit and interpretable in most machine learning models [1].

The project will look at models such as spiking neural networks (SNNs) and consider the problems of knowledge representation and localised model editing. SNNs have been considered the third generation of ANNs which, besides being more biologically realistic, are characterised by data sparsity and power efficiency [2]. The ability of today’s hardware to emulate biological neurons is constantly improving [3]. Being able to characterise the representation of knowledge in such networks has a strong impact on efficient AI as well as enabling better hypotheses for knowledge representation in the brain. The project is expected to lead to publications in top AI and Computational Neuroscience conferences and journals.

Entry Requirements

Applicants must have, or be about to obtain, a UK Honours degree 1st or 2.1, or international equivalent, in a discipline directly relevant to the PhD. A master’s level qualification would also be advantageous.

Non-UK applicants, who are not currently studying in the UK, must meet the programme’s English language requirement before the application deadline – no exceptions will be considered.

Fees

Applicants will be considered for a studentship. Studentships cover tuition fees, provide a £1k per annum training support fee, and a maintenance stipend (£21,805 2026/27 rate). International students will be required to cover the cost of a Student visa and Immigration Health Surcharge from personal funds.

Who is eligible to apply?

The Faculty of Engineering and Design at the University of Bath has studentships available for Home and Overseas students. However, in line with UKRI policy, the number of studentships available to Overseas students are capped.

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