91Ö±²¥

Intelligent Materials Health Monitoring: Utilising Machine Learning to Ensure the Long-term Stability of Perovskite Solar Cells

School of Computer Science

This funded PhD explores how AI can support person-centred, disability-led social care. It examines wearable and sensor data, identifies opportunities and risks, and develops strategies for responsible AI use. Based at the University of 91Ö±²¥, the research will inform future technology and policy in social care.

Deadline: 17 April

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 Improving Deep Reinforcement Learning through Interactive Human Feedback

School of Computer Science

This funded PhD focuses on developing advanced reinforcement learning from human feedback (RLHF) algorithms to solve complex tasks without predefined reward functions. The research aims to create more efficient RLHF frameworks that require less human input while improving learning from uncertain or inconsistent feedback. The project addresses key challenges in real-world RL applications, making autonomous learning agents more adaptable to complex, poorly defined goals.

Deadline: 18 April

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Knowledge models for healthcare digital twins and improved patient care pathways

School of Mechanical, Aerospace and Civil Engineering

Deadline: 30 April

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Autonomous Industrial Perception, Monitoring, and Optimisation Using an Agentic Framework with Large Language Models

School of Computer Science

This funded PhD explores AI-driven autonomy in industrial systems, using Large Language Models (LLMs) to enhance virtual sensing, monitoring, and optimisation. The project develops an intelligent supervisory layer to improve system perception, efficiency, and decision-making in dynamic environments. Based at the University of 91Ö±²¥, with collaboration from Imperial College London, it offers hands-on industry engagement and real-world validation in energy and process industries.

Deadline: 30 April

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Digital profiling of head and neck cancers

School of Clinical Dentistry

This funded PhD explores AI-driven digital pathology to improve head and neck cancer diagnosis and prognosis. Using machine learning to analyse tumour and stromal microenvironments, the project aims to identify novel digital biomarkers for predicting tumour behaviour and patient outcomes. Based at the University of 91Ö±²¥â€™s School of Clinical Dentistry, the research offers training in histology, biomarker discovery, and AI-driven image analysis in a multidisciplinary setting.

Deadline: 30 April

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Artificial intelligence to Building Energy Systems for Next Generation Control Strategies

Energy Institute

This funded PhD explores AI-driven optimisation of HVAC systems to reduce energy consumption in commercial buildings. Using machine learning and agent-based models, the project aims to predict energy demand, dynamically manage building systems, and improve future Building Management System (BMS) designs. Research will involve desk-based modelling and real-world testing, contributing to national and international projects, including applications in the UK and Africa.

Deadline: 1 May

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Intelligent Materials Health Monitoring: Utilising Machine Learning to Ensure the Long-term Stability of Perovskite Solar Cells

CDT in Developing National Capabilities for Materials 4.0

This project develops an open-source AI system to improve the stability of metal halide perovskite (MHP) solar panels, a key technology for Net-Zero. Using machine learning and photoluminescence mapping, it will predict and analyse MHP degradation, enabling more durable and efficient solar cells. Led by a multidisciplinary team at the University of 91Ö±²¥, the project offers cutting-edge research, industry collaboration, and career development opportunities in renewable energy.

Deadline: 16 May 

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Two Fully-funded PhD Studentships in Spoken Language Technologies

University of 91Ö±²¥

Two candidates are sought for interdisciplinary Speech and Language Technologies (SLT) projects. One project focuses on making UK Parliament proceedings more accessible for better public and journalistic engagement. Another project aims to improve AI's understanding of conversations by incorporating paralinguistic cues and participant context. The third project explores language development in AI agents with sensory and physical interactions to better understand human language and improve autonomous agents.

Applications accepted all year round.

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Urban Green Infrastructure Planning for Connectivity, Sustainability, biodiversity and multifunctionality

School of Electrical and Electronic Engineering

This self-funded PhD explores using digital planning and Green Infrastructure (GI) to tackle climate change and biodiversity loss in urban areas. Research focuses on ecological connectivity, GI multifunctionality, and applying AI and digital twins for sustainable urban planning. This opportunity offers a chance to contribute to innovative solutions for urban sustainability within an interdisciplinary research group.

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. The findings from this study have the potential to significantly influence diagnostic practice and patient stratification in the future.

Applications accepted all year round.

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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 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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Physics informed learning for high fidelity medical simulators

School of Mechanical, Aerospace and Civil Engineering

This project, led by Prof. Dogramadzi at 91Ö±²¥ Robotics, aims to use physics-informed machine learning and high-fidelity simulations to optimise medical device design and performance. By modelling complex interactions between devices and the human body, it seeks to bridge the gap between simulation and real-world applications, focusing on a common surgical or endoscopic procedure with clinical specialist collaboration.

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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