Richard Allmendinger

A fully funded PhD position is available at the University of Manchester within the UKRI AI Centre for Doctoral Training (CDT) in Decision Making for Complex Systems. This 4-year programme aims to train future AI researchers, with a specific project on Adaptive Multi-Objective Search in Expensive High-Dimensional Socio-Technical Systems. The research, in collaboration with Honda Research Institute Europe, will focus on developing adaptive optimisation frameworks for complex socio-technical systems, particularly energy distribution networks for electric vehicles. Key research themes include artificial intelligence, machine learning, high-dimensional optimisation, Bayesian optimisation, Gaussian processes, operational research, and decision support. The CDT unites researchers from Manchester and Cambridge, applying AI and machine learning to real-world challenges in engineering, physics, biology, and business. The position is fully funded, and applicants from diverse backgrounds with a passion for AI and decision-making in complex systems are encouraged to apply. The application deadline is 5 December 2025. For more information and to apply, visit the provided link.

This fully funded PhD by Enterprise project at The University of Manchester, titled 'Agentic Intelligence for Decision-making in Investment and Enterprise (AIDE)', offers a unique opportunity to advance research at the intersection of artificial intelligence, decision sciences, and real-world innovation ecosystems. The project addresses the growing complexity and data-intensity of investment and venture evaluation environments, such as venture capital, private equity, and university innovation ecosystems, where decision-making remains fragmented and manual despite abundant information. The research aims to develop next-generation AI systems that support holistic, data-driven, and uncertainty-aware decision-making. A key focus is the design and development of knowledge graphs to structure and connect heterogeneous data sources, enabling richer contextual understanding and reasoning. The project will explore explainable AI (XAI) methods to ensure transparency, interpretability, and user trust in high-stakes investment decisions, allowing users to interrogate, justify, and adapt recommendations. This includes surfacing key evidence, assumptions, and uncertainties underpinning each step of the decision process, potentially leveraging knowledge graph structures to trace relationships and reasoning paths across data. Research will investigate integrating diverse information sources—structured financial data, textual documents, company disclosures, and online signals—into unified representations that support robust reasoning. Techniques for quantifying and propagating uncertainty across multi-stage workflows will be examined, enabling users to explore how assumptions or market changes affect outcomes. The project also studies how multiple AI agents can collaborate to reflect real-world investment workflows, coordinating tasks such as screening, due-diligence analysis, risk assessment, and scenario modelling, with knowledge graphs serving as a shared structured memory and coordination layer. Methodologically, the project blends machine learning, probabilistic modelling, multi-agent systems, explainable AI, human-computer interaction, knowledge representation, and graph-based reasoning. A design-science research approach will be used, with iterative prototyping, evaluation using realistic scenarios, and engagement with practitioners from investment and innovation communities. Applicants should hold a Bachelor's (Honours) degree at 2:1 or above (or overseas equivalent), and normally a Master's degree in a relevant cognate subject with an overall average of 65% or above. Professional qualifications and/or relevant experience may be considered. Desirable backgrounds include Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Mathematics, Engineering, Information Systems, or related disciplines. Strong analytical and programming skills (e.g., Python, machine learning frameworks) are advantageous. Familiarity with knowledge graphs, graph databases, semantic technologies, natural language processing, probabilistic modelling, multi-agent systems, or human-AI interaction is beneficial but not required. Candidates from numerate disciplines with professional experience in data science, analytics, financial technology, investment analysis, or innovation ecosystems are encouraged to apply. English language requirements: IELTS minimum 7.0 overall (6.5 in other sections), TOEFL minimum 100 overall (25 in all sections), PTE minimum 76 overall (76 in writing, 70 in other sections), or evidence of a degree from a majority English-speaking nation within the last 5 years. Supporting documents required: academic transcripts and certificates, academic CV, supporting statement (max 700 words), writing sample (up to 5,000 words), and two academic referees. A PhD proposal is not required. The studentship is fully funded, covering tuition fees, UKRI stipend (£21,805 per annum for 2026/27), and Research Training Support Grant (RTSG). The project is based in the Faculty of Humanities Doctoral Academy, Alliance Manchester Business School. The application deadline is May 29, 2026. For further information or discussion, contact Prof Richard Allmendinger ([email protected]).