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We invite applications for a self-designed doctoral project to critically examine Responsible AI, an essential but contested concept for ensuring ethical and equitable technology. While it aims for safety and fairness, critics argue that current discourse often fails to address structural inequities and power imbalances, leading to a significant participation gap that compromises genuine social justice. Your interdisciplinary research, hosted by the Centre for Machine Intelligence and the School of Sociological Studies, Politics and International Relations, will be supervised by Dr. Susan Oman, the AI and In/equality Lead in the Centre for Machine Intelligence. The project must be grounded in contemporary AI ethics debates and demonstrate a core commitment to equity, using innovative methods to define and challenge who benefits from AI and who gets to decide what 'responsible' truly means.

Join the world-leading EPSRC Doctoral Landscape Award research in collaboration with Digital Science to revolutionize how AI is used across scientific disciplines. You will leverage NLP and Large Language Models (like Gemini) to develop new AI tools for analyzing scientific literature, bridging knowledge gaps, and accelerating research innovation in areas like health and manufacturing. Supervised by Dr. Denis Newman-Griffis, the AI for Health Lead in the Centre for Machine Intelligence and Prof. Mike Thelwall, this opportunity offers top-tier academic research and crucial industry experience, positioning you at the forefront of the AI revolution in science. core commitment to equity, using innovative methods to define and challenge who benefits from AI and who gets to decide what 'responsible' truly means.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading research opportunity to safeguard critical civil infrastructure (like bridges) by enhancing the resilience of Structural Health Monitoring (SHM) systems. The project tackles the urgent problem of cyber-physical attacks against the AI and Machine Learning models used for maintenance. Supervised by Dr. Max Champneys (Fellow in AI for Sustainability and Resilience at the Centre for Machine Intelligence) and others, you will explore various digital and physical threat modalities, investigate vulnerabilities to adversarial machine learning, and develop mitigations to prevent system exploitation, ultimately improving public safety and securing national infrastructure.

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.

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.

This fully funded PhD position at the University of Sheffield's world-leading NLP Group offers a unique research opportunity under the supervision of Dr. Delvin Ce Zhang. The project tackles the urgent challenge of misinformation on social media by developing next-generation retrieval-augmented, multi-modal, and explainable Large Language Models (LLMs). You will design a novel Vision-Language Model (VLM)

This fully funded PhD position at the University of Sheffield offers an interdisciplinary opportunity to combat pollinator decline. The project uses Machine Learning (ML), including Bayesian ML, to develop a novel method for tracking and analyzing the movement of flying insects (e.g., bumblebees) across landscapes. This research is part of a major grant covering hardware and field experiments, and provides highly sought-after expertise in probabilistic ML and AI, with excellent potential for spinout opportunities.

This fully funded PhD position at the University of Sheffield's Intelligent Manipulation Lab (Sheffield Robotics), supervised by Associate Professor Amir Ghalamzan, will advance contact-rich robotic manipulation. The project focuses on fusing high-rate tactile and vision sensing to develop predictive control algorithms (using sampling-based MPC) and world models that anticipate slip and deformation in sub-100 ms horizons. You will also explore how foundation vision-language models can translate high-level tasks into low-level control, enabling robots to safely and efficiently handle deformable objects and execute robust grasping tasks in unstructured settings.

Train the next generation of AI! Secure an EPSRC Doctoral Landscape Award to research advanced Reinforcement Learning (RL), focusing on how to reliably align autonomous agents with human values. This project will develop novel RLHF methods by efficiently combining human and AI feedback (RLAIF) using cutting-edge Large Language Models and Deep Reinforcement Learning. This is a high-impact opportunity to gain expertise in core AI alignment technologies essential for the future of AI in human society.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading, mathematically rich opportunity to design new algorithms and theory for value-based optimal stopping problems. Inspired by biological systems, the project focuses on three computational challenges: developing novel Inverse Reinforcement Learning (IRL) algorithms to uncover hidden reward functions; conducting theoretical analysis of efficient decision policies; and creating high-performance, parallelized numerical solutions to complex Bayesian optimal stopping problems. This research is ideal for candidates with strong mathematical ability and an interest in applying control theory and AI principles to critical real-world challenges.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to ensure human safety as complex, autonomous robots operate alongside people. The project develops a revolutionary approach to robot safety by focusing on identifying the safe bounds of human and robot behaviours, rather than pre-defining every hazard. You will develop a hazard assessment methodology implemented in a digital twin of a robotic system, with support from a University spinout and the Health and Safety Executive (HSE), offering direct real-world impact on robotic safety standards.

Design the Future of Learning with AI! Secure an EPSRC Doctoral Landscape Award to develop multi-agent AI systems that replace the single-assistant model in education. This interdisciplinary project, based in Computer Science with the School of Education, will create a team of AI personas (e.g., Challenger, Supporter) to foster collaborative learning, critical thinking, and enhanced writing/language skills. This is a key opportunity to pioneer ethical and effective AI in Education (AI4Ed).

Build Trustworthy AI for Healthcare! Secure an EPSRC Doctoral Landscape Award at the University of Sheffield to pioneer Neuro-Symbolic AI for evidence-based clinical reasoning. This project tackles the critical gap between linguistically fluent LLMs and the need for verifiable, rational decisions in high-stakes healthcare. Supervised by Dr. Marco Valentino and Dr. Xi Wang, you will integrate the linguistic power of LLMs with formal logic to create auditable, explainable AI for applications like clinical NLP and medical question answering, significantly enhancing patient safety and reducing clinical workload.

Secure an EPSRC Doctoral Landscape Award at the University of Sheffield to lead innovative research into user trust in future technologies, like assistive robots. This project will develop a novel experimental paradigm that uses Generative AI (for video, images, and text) to create large-scale, personalized "design fictions" about future tech scenarios. Working with North Yorkshire Council, you will analyse public attitudes and design factors to inform the creation of inclusive and trustworthy robotic systems, gaining expertise in AI, participatory design, and statistical analysis.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading research opportunity to tackle global hypertension by pioneering a non-invasive, continuous blood pressure (BP) monitoring system. The project uses Deep Learning AI to directly analyze Photoplethysmography (PPG) signals—technology embedded in popular wearables. You will develop multimodal AI models integrating PPG with clinical data, creating a scalable solution that eliminates the need for extra sensors and makes continuous, personalized heart health monitoring an easy, everyday reality.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading research opportunity to enhance the trustworthiness of AI by developing an Explainable AI (XAI) framework for Visual Analytics (VA). The project addresses the critical need for robust causal inference in decision-making, helping users reason about cause-and-effect rather than just correlation. Collaborating with industry, you will apply the framework to regenerative and climate-smart agriculture, directly advancing sustainable practices and reducing greenhouse gas emissions.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to develop a novel multi-modal AI framework to automatically fact-check spoken healthcare claims. The project focuses on creating a joint Speech-Language Model that verifies audio claims against textual evidence (e.g., medical literature). In collaboration with the Sheffield Teaching Hospital, this vital research advances fact-checking by incorporating elements like tone and prosody to enhance information reliability and support evidence-based clinical decision-making.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to advance cardiovascular disease (CVD) care using Artificial Intelligence (AI). The project will develop robust generative modelling techniques for multi-modal data fusion (combining scans and clinical records) to create highly detailed, 3D heart models. This research provides clinicians with trustworthy, personalised assessments of heart function, directly supporting earlier detection and improved patient management for CVD.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to enhance the reliability of Large Language Models (LLMs) in software engineering. The project addresses the critical issue of subtle errors in AI-generated code by unifying code generation and automated testing. You will develop both systematic testing methods and intelligent coding assistants that learn from their mistakes, creating dependable, adaptive AI systems essential for safe and responsible software development.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to fuse Materials Science and Artificial Intelligence. The project focuses on creating fast, reliable Physics-Informed Machine Learning models to simulate magnetic quasi-particles (skyrmions), critical for next-generation computing. You will embed physical and topological constraints into advanced AI architectures to enable the high-throughput discovery and rapid prototyping of novel magnetic devices.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to transform the monitoring of Ataxia, a disabling neurological condition. The project will develop objective, AI-driven tools using speech technology and machine learning to analyse changes in ataxic dysarthria. Working with the Sheffield Ataxia Centre, the research will create a system that tracks disease progression more sensitively and enhance speech recognition for disordered voices, contributing to new AI foundation models and accelerating future clinical trials.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to tackle the urgent problem of "content smuggling" in open-source Generative AI models (LLMs). The project will create the first framework to detect and prevent harmful or copyrighted material hidden within models. You will develop both forensic tools to scan for covert content signatures and proactive architectural defences, combining technical innovation with legal and ethical compliance to safeguard intellectual property and public safety in open AI.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading research opportunity, led by Dr. Xiancheng Yu, to transform patient care for intracranial haemorrhage (ICH). The project will develop new computational tools using Physics-Informed Neural Networks (PINNs) to accurately and explainably predict cerebral blood circulation and injury progression following a brain bleed. By embedding physiological laws into machine learning models, and validating them against clinical data, the research will identify biomarkers to guide crucial treatment decisions, ultimately improving patient prognosis for this life-threatening condition.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to solve the dramatic energy consumption challenge of modern AI. The project will pioneer new neuromorphic hardware by developing the first physical implementation of Kolmogorov–Arnold Networks (KANs) using nanoscale magnetic (spintronic) materials. This research combines modelling and experimentation to enable brain-like computation with drastically lower power consumption for next-generation intelligent edge devices.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to transform emergency healthcare with a vision-based diagnostic aid for stroke. The project will use computer vision and machine learning to analyze subtle facial movements for rapid, objective stroke detection suitable for ambulances. Working with NHS clinicians, this research aims to ensure patients receive treatment within the critical window, significantly improving outcomes for this severe condition.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to improve the reliability of electric motors by solving the challenge of data scarcity in condition monitoring. The project will develop an innovative fleet-based machine learning methodology, pioneering Deep Learning models to accurately detect faults using raw, time-domain signals. This interdisciplinary research will also develop techniques to transfer models and data between different motors, enabling reliable, cost-effective, and timely fault detection across all industrial applications.

This EPSRC Doctoral Landscape Award at the University of Sheffield offers a world-leading opportunity to develop a revolutionary quantum sensing microscope to observe and measure how bacteria interact with the immune system. You will create highly sensitive probes by labeling bacteria with light-emitting proteins, using an advanced AI data processing pipeline to measure environmental stress (like pH) inside host defence cells. This interdisciplinary project pioneers the use of quantum sensing and AI to gain unprecedented detail on bacterial life.

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.

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.