[School of Engineering PhD Scholarships] Risk-Informed Human–AI Collaboration in Construction Projects: Dynamic Allocation of Decision-Making and Control

About the project

About the Project Artificial intelligence is increasingly being used in construction to support planning, risk prediction, monitoring and operational decision-making. As AI systems become more capable, an important question is emerging: how should humans and AI work together, and when should decision-making or control shift between them? In many current applications, the respective roles of the human and AI are set in advance—for example, AI provides a recommendation and the human makes the final decision. However, this may not always be appropriate. The level of human or AI involvement may need to change depending on factors such as task risk, uncertainty, AI reliability, human expertise and changing project conditions. This PhD will investigate how decision-making and control can be dynamically allocated between humans and AI in construction projects. The research will explore when different human–AI arrangements may become ineffective or unsafe, develop risk-informed approaches for determining when greater human or AI involvement is required, and test whether dynamic arrangements can improve safety, reliability and operational performance compared with fixed approaches. The project will combine engineering analysis with human-in-the-loop experiments and simulation. The student will work with real construction scenarios and industry data to identify potential failure modes and situations that may require a change in human or AI involvement. These insights will be used to develop and evaluate quantitative risk and decision models. Depending on the direction of the research, the project may involve simulation, AI-enabled decision-support systems, experimental studies and computational modelling. A major strength of the project is its close engagement with industry. Murphy, Mace and United Infrastructure, all Tier 1 construction contractors, together with engineering consultancy Curtins, will provide project data, practitioner input and support for the development and validation of realistic construction scenarios. The successful candidate will join an interdisciplinary supervisory team with expertise spanning engineering management, AI and digital transformation, safety and reliability engineering, and social and organisational sciences. Training will be provided in human–AI systems, risk and reliability analysis, experimental methods, statistical analysis, responsible AI and relevant computational techniques. This project would particularly suit a candidate interested in AI, engineering systems, construction, safety, decision-making and human–technology interaction, and in developing research with both academic and practical impact. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD such as civil or construction engineering, engineering management, computer science, artificial intelligence/data science, systems engineering or safety/reliability engineering (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD such as civil or construction engineering, engineering management, computer science, artificial intelligence/data science, systems engineering or safety/reliability engineering (or international equivalent). Previous research experience in quantitative methods, programming, data analysis, simulation, AI/ML, or experimental research is desirable. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

Research areas