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We have 24 Software Engineering PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Bath
Software Engineering PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Bath
We have 24 Software Engineering PhD Projects, Programmes & Scholarships PhD Projects, Programmes & Scholarships in Bath
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Statistical machine learning for computational neuroscience
This project will focus on signal processing in neuroscience applications using state-of-the-art statistical machine learning techniques. Read more
Safety assured Bayesian machine learning for reliable decision making in autonomous driving
Autonomous driving systems (ADS) using artificial intelligence techniques have seen rapid development in recent years. Autonomous driving is expected to reshape the current transportation system and may significantly impact human travel in the near future. Read more
Explainable artificial intelligence in Bayesian machine learning research and applications
Artificial intelligence (AI) has been successfully applied to various applications with the breakthrough of deep learning (DL) in recent years. Read more
Transparency in neural ordinary differential equations
Neural ordinary differential equations (neural ODEs) are a relatively recent development (. https://arxiv.org/abs/1806.07366). in the field of machine learning, where the hidden state dynamics of certain neural network architectures can be reformulated as numerical solutions of a differential equation. Read more
The diversity challenge for leading companies: application of AI technologies in recruitment and selection
This interdisciplinary PhD project addresses the diversity challenge for leading companies by critically questioning the use of AI in processes of recruitment and selection. Read more
Rendering AI more transparent and explainable: Which explanations do humans prefer?
Despite successful applications of artificial intelligence (AI) in object labelling or face recognition tasks, individuals, companies, and governmental agencies are still wary of a wide deployment of AI. Read more
Neuromorphic tactile interface for human-robot interaction
The sense of touch is an essential component for the development of robotics and autonomous systems capable of interacting safely with humans and their surrounding environment. Read more
Neuro-Symbolic Artificial Intelligence for Efficient and Interpretable Natural Language Understanding
The significant, and by some measures superhuman [1], performance of deep neural models has led to their wide adoption across multiple fields including Natural Language Processing. Read more
Multimodal active perception for safe autonomous vehicles
Robotic active perception is a key factor to develop autonomous systems capable of safely and intelligently interacting with humans and their surrounding environment. Read more
Hierarchical reinforcement learning
One of the most fundamental problems in artificial intelligence (AI) is how to enable autonomous development of action hierarchies, in other words, how to endow artificial agents with the ability to autonomously form useful high-level behaviours (for example, grasping) from existing behavioural units (for example, primitive sensory and motor actions available to a robot). Read more
An AI all-weather look-out for autonomous marine navigation
Autonomous maritime navigation is set to transform the maritime industry providing consistent, continuous and cost-effective navigation free from human error. Read more
Understanding large language models
Large language models such as GPT and FLAN are increasingly being used as a foundation in a number of AI applications. They are created by training a machine learning architecture known as a “Transformer” on very large amounts of natural text. Read more
Explainable Natural Language Processing for the Analysis of Online Discourse
Recent advances in Natural Language Processing (NLP), have relied, in large part, on deep neural models which consist of hundreds of millions (and often billions) of parameters. Read more
Explainability in Multimodal Deep Learning: Transparent Fusion of Text and Images
Despite the significant performance gains across a range of tasks in both vision and Natural Language Processing (NLP), deep neural models are far from being able to reason effectively [1]. Read more
Explainability in Extreme Classification
Classification of data to a few relevant categories selected from an extremely large number of all possible labels, also referred to as Extreme Classification, has found numerous applications including recommendation systems, information retrieval and Question-Answering. Read more
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