PhD Study
The University of Sheffield offers various PhD programmes with different opportunities to study Artificial Intelligence.
PhD opportunities
Design of a Fault Detection System for AI-Assisted Adversarial Attacks on Industrial Control Systems
School of Computer Science
This PhD project aims to secure industrial control systems (ICS) from AI-assisted adversarial attacks. The research will involve designing a novel fault detection system to identify these attacks by using advanced techniques like multi-agent deep reinforcement learning (MDRL). The project will explore how AI adversaries manipulate industrial environments and develop robust defences to protect critical infrastructure, contributing to the future of smart industries.
Deadline: Friday 31 October
Find out more information and apply here.
Improving Deep Reinforcement Learning through Interactive Human Feedback
School of Computer Science
This PhD project is about developing new reinforcement learning from human feedback (RLHF) algorithms. The primary goal is to solve complex tasks for AI agents without needing a predefined reward function, a major challenge in deep reinforcement learning. The research will focus on creating a new RLHF framework that can learn complex behaviours with much less human input than current methods by extracting more information from uncertain or inconsistent feedback. The project is flexible and can explore applications like fine-tuning large language models (LLMs) and robotics, depending on the student's interests.
Deadline: Friday 31 October
Find out more information and apply here.
AI-Enhanced Healthcare - Leveraging Multimodal Data Integration for Impactful Innovation
School of Electrical and Electronic Engineering
This PhD project uses AI and data engineering to advance personalized medicine. The main goal is to create a system that can combine different types of patient data - like scans, smartwatch readings, and doctor's notes—to develop tailored treatments. The project will address the critical challenge of maintaining patient privacy by building privacy-preserving features directly into the AI models. The ultimate aim is to show that precision medicine and data security can both be achieved, leading to better patient outcomes without compromising confidentiality.
Applications accepted all year round.
Find out more information and apply here.
Shifting the paradigm: machine-assisted scholarly digital editing
Digital Humanities Institute
This PhD project investigates how AI and machine learning can transform scholarly digital editing—streamlining editorial workflows and reimagining digital editions beyond traditional print formats. Candidates will explore tools such as NLP, large language models, and data visualisation to enhance and innovate the presentation of historical or cultural texts. Applicants are invited to bring their own case studies and subject expertise to advance the field of digital editing.
Applications accepted all year round.
Find out more information and apply here.
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.
Find out more information and apply here.
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 Sheffield, 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.
Find out more information and apply here.
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.
Find out more information and apply here.
Physics informed learning for high fidelity medical simulators
School of Mechanical, Aerospace and Civil Engineering
This project, led by Prof. Dogramadzi at Sheffield 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.
Find out more information and apply here.