91Ö±²¥

Trust in Distributed Surveillance Systems

School of Electrical and Electronic Engineering

Are you interested in Machine Learning and Signal Processing methods and their applications to autonomous systems and surveillance? We have a PhD project funded by the UK Government Defence Science and Technology Laboratory (DSTL) at the University of 91Ö±²¥, a leading World University and a Russell Group University in the UK.

Deadline: Tuesday 31 December 2024

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Foundation Models for Manufacturing (S3.5-COM-Ragni)

School of Computer Science

This PhD project aims to explore the feasibility of foundation AI models for predictive manufacturing tasks, addressing challenges posed by the industry's diverse and heterogeneous nature. The project will focus on developing AI models that can handle multiple data modalities and benefit from self-supervised learning, enabling more effective predictive solutions in manufacturing, while reducing reliance on large, labelled datasets.

Deadline: Wednesday 29 January 2025

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AI-Enhanced Healthcare - Leveraging Multimodal Data Integration for Impactful Innovation - S3.5-ELE-Eissa

School of Electrical and Electronic Engineering

The integration of AI into healthcare is transforming the field by enabling data-driven decision-making, enhancing diagnostics, personalising treatments, and empowering patients through real-time monitoring. The PhD project will focus on advancing data engineering frameworks to process and integrate diverse healthcare data, contributing to more effective, efficient, and patient-centric healthcare solutions.

Deadline: Wednesday 29 January 2025

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Advancing ECG Interpretation with Human-Centred Multi-modal AI (S3.5-COM-CChen)

School of Computer Science

This PhD project aims to address healthcare challenges in cardiovascular disease diagnosis through innovative Human-Centred Artificial Intelligence (HCAI) approaches, focusing on improving ECG interpretation accuracy and overcoming limitations of current AI tools. The project will develop adaptive applications that integrate diverse ECG data sources, multimodal learning for enhanced decision-making, and an explainable HCAI tool to support clinicians in providing more accurate, personalized, and transparent cardiac diagnoses.

Deadline: Wednesday 29 January 2025

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 AI-Grid: Converter-based renewable generation and AI-enabled network control for operation stability of cyber-physical power grid - C3.5-ELE-Zhang

School of Electrical and Electronic Engineering

This PhD project focuses on integrating AI-enabled network control with converter-controlled renewable generation to enhance frequency stability and voltage regulation in future power grids. The research will involve developing smart converter-based energy techniques, AI-based network control algorithms, and validating their impact on grid stability through simulations and industrial power systems, ultimately contributing to the transformation of the grid into an autonomous, cyber-physical system.

Deadline: Wednesday 29 January 2025

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Energy Efficient Secure Hardware Acceleration of AI Models for Edge Devices in Healthcare Applications - S3.5-ELE-Benaissa

School of Electrical and Electronic Engineering

This PhD project focuses on addressing the critical challenges of data privacy, security, and computational efficiency in AI-driven healthcare applications. The candidate will develop secure, energy-efficient AI models deployed on dedicated hardware platforms, ensuring robust data pipelines, real-time performance, and seamless integration within healthcare systems, with a focus on hardware-software co-design and countermeasures against data breaches.

Deadline: Wednesday 29 January 2025

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AI-Driven Adaptive Cognitive Rehabilitation at Home: Neuroimaging-Enhanced Approaches for Stroke and Beyond - C3.5-ELE-Arvaneh

School of Electrical and Electronic Engineering

This PhD project aims to develop AI models that enable autonomous, adaptive use of MindLenses, a tool for cognitive rehabilitation in stroke patients, by personalising therapy through EEG data and behavioural analysis. The project will focus on creating AI algorithms to replicate clinician decision-making for home-based, real-time cognitive exercises, while addressing challenges such as EEG signal variability and ensuring accessible, user-centred therapy.

Deadline: Wednesday 29 January 2025

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Integrating machine-learning techniques to learn interactions between structural components and physical domains

School of Mechanical, Aerospace and Civil Engineering

The project aims at evaluating the performance of different machine-learning methods for the learning the desired interactions and for defining digital twins of structures. The methods may span from simple techniques to more complicated deep-learning techniques and the main objective is to integrate different types of data and even various types of models in the inference procedure. 

Deadline: Friday 31 January 2025

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AI for Multi-modal Healthcare

School of Computer Science

A 3.5-year funded PhD studentship is available at the University of 91Ö±²¥, focusing on multi-modal AI for healthcare. The project aims to develop AI models capable of handling complex medical data, addressing domain and knowledge gaps, and adapting to individual patient needs, with potential emphasis on large language models, explainable AI, and identifying pitfalls in current AI models through adversarial machine learning.

Applications accepted all year round.

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Machine Learning for Resilient Operation of Cybersecurity Systems

School of Electrical and Electronic Engineering

Are you interested in machine learning and data science and their application to cybersecurity? We have a PhD project fully funded by the DSTL (Defence Science and Technology Laboratory) at 91Ö±²¥, part of the prestigious UK Russell Group of Universities in the UK.

Applications accepted all year round.

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 ACS-99-OPEN - Machine tool dynamics-based digital twins for real-time monitoring of cutting tool conditions in smart manufacturing

School of Electrical and Electronic Engineering

This PhD project focuses on advancing a novel, model feature-based tool condition monitoring (TCM) technique to meet the needs of real-time monitoring in complex machining environments. By collaborating with industry leaders and the Advanced Manufacturing Research Centre (AMRC) in 91Ö±²¥, the research aims to improve the adaptability and efficiency of TCM systems, moving them to higher Technology Readiness Levels (TRLs) for practical industrial applications.

Applications accepted all year round.

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Adaptive Learning in Brain-Robot Interactions

School of Electrical and Electronic Engineering

This project aims to develop a non-invasive brain-machine interface (BMI) that allows a user to direct a semi-autonomous robot to perform different tasks through brain signals.

Applications accepted all year round.

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Joining up Solar and Stellar Flare Energy Estimates

School of Electrical and Electronic Engineering

We seek candidates to advance solar-stellar flare research by addressing challenges in energy estimation methodologies and aligning solar and stellar observational techniques. This project aims to validate new estimation techniques, integrate machine learning, analyse energy frequency mismatches, study "solar-like" stars, and update long-term super-flare probability estimates for our Sun.

Applications accepted all year round.

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AI-enabled digital technologies for the resilient operation of power systems

School of Electrical and Electronic Engineering

Are you interested in power system research for the future power grid operation? We have a recent PhD project opportunity at 91Ö±²¥, a World leading Russell Group university in the UK.

Applications accepted all year round.

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Machine Learning for radiative transfer applications in multidimensional flare simulations

School of Electrical and Electronic Engineering

This project will combine the strengths of the University of 91Ö±²¥â€™s ACSE department (expertise in Machine Learning), with the world leading 1D flare modelling expertise of NASA Goddard Space Flight Center.

Applications accepted all year round.

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Development of a Wearable Sensor System and AI-Driven Analysis for Objective Bruxism Assessment

School of Clinical Dentistry

This PhD project aims to develop and validate a novel wearable sensor system for comprehensive bruxism assessment.

Applications accepted all year round.

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Automated Detection and Analysis of Cancers using Artificial Intelligence

School of Clinical Dentistry

The aim of this project is to investigate the use of artificial intelligence and machine learning in automated detection and segmentation of cancer and its microenvironment for downstream prognostic analysis. Analysis of histomorphological as well as molecular/genomic features will also be performed.

Applications accepted all year round.

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Developing AI Controlled Granulation Process for Formulated Chemicals

School of Chemical, Materials and Biological Engineering

The aim of this project is to use Industry 4.0 technologies including machine learning and artificial intelligence (AI) to develop digital and soft sensors to predict product properties and optimise process in real-time for manufacturing functional chemical and/or pharmaceutical products

Applications accepted all year round.

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Energy-efficient AI Using Modular Deep Reservoir Computing

School of Computer Science

This project aims to explore how diverse properties in recurrent neural networks can be used to create reservoir computing architectures able to tackle challenging real-world tasks.

Applications accepted all year round.

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Real-Time Brain Injury Prediction and Protection Framework for Intelligent Vehicles

Department of Mechanical Engineering

We are seeking a highly motivated PhD candidate to join our research project at the University of 91Ö±²¥, focusing on the development of intelligent vehicle safety strategies with a focus on brain injury prediction. 

Applications accepted all year round.

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Statistics and AI for Engineering and Smart Manufacturing

School of Mathematics and Statistics

We are thrilled to announce an exceptional PhD opportunity in the dynamic fields of Statistics and Artificial Intelligence, specifically tailored for Engineering and Smart Manufacturing. This project is designed for candidates eager to explore and expand the realms of statistical analysis and reliable AI in industrial pipelines, with a special emphasis on smart manufacturing and electronic design automation (EDA).

Applications accepted all year round.

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Online discussion; augmenting argumentation with chatbots

Department of Psychology

Argumentation - the systematic exchange of reasoning supporting or undermining an idea - enhances communication between individuals. Unreliable chains of thought are weeded out, reliable ones survive. Striking evidence for this is that reasonings tasks which provoke systematic errors when considered by individuals choice can be solved correctly by small groups, if they are given time for discussion. Chatbots with natural language processing create an opportunity to have artificial agents interact with group deliberation, and make it more effective. In this way, the strengths of human and artificial intelligence can augment each other

Applications accepted all year round.

Find out more information and apply .