PhD Studentship - AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise

Closes: 29^th May 2026 (midnight)

PhD by Enterprise

The University of Manchester’s PhD by Enterprise is a new four‑year doctoral programme that combines world‑class research with structured entrepreneurship training. The programme enables the University’s research portfolio to generate tangible economic, environmental and societal impact through venture creation and enterprise-led pathways.

The programme includes a fully funded studentship to commence in September 2026, covering tuition fees, UKRI stipend (2026/27 rate £21,805 per annum) and Research Training Support Grant.

Project details: AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise

Investment and venture evaluation contexts—such as venture capital, private equity, and university innovation ecosystems—are increasingly data intensive, yet decision-making across deal sourcing, evaluation, due diligence, and post‑investment monitoring remains fragmented and largely manual. While existing platforms support data aggregation and search, they offer limited capabilities for deeper reasoning, uncertainty management, or coordinated, lifecycle‑wide decision support.

This PhD project aims to develop next‑generation AI systems that enable holistic, data‑driven, and uncertainty‑aware decision-making in high‑stakes investment environments. Central to the research is the design and use of knowledge graphs to structure and connect heterogeneous data sources—such as financial data, company disclosures, textual reports, and online signals—supporting richer contextual understanding and reasoning.

A key emphasis is explainable AI (XAI). Investment decisions require transparency and trust, and the project will investigate methods that allow users to interrogate recommendations by exposing the evidence, assumptions, relationships, and uncertainties underpinning each stage of the decision process. The research will also address uncertainty modelling, enabling users to explore how changing assumptions or market conditions affect outcomes across multi‑stage workflows.

The project will further examine multi‑agent AI systems that mirror real‑world investment processes, with agents collaborating on tasks such as screening, due diligence, risk assessment, and scenario analysis, potentially using knowledge graphs as a shared coordination and memory layer. Human‑AI collaboration will be central, ensuring users retain control and oversight.

Methodologically, the research integrates machine learning, probabilistic modelling, explainable AI, multi‑agent systems, knowledge representation, and human‑computer interaction, using a design‑science approach grounded in realistic investment scenarios and practitioner engagement.

Academic Criteria:

• Bachelor's (Honours) degree at 2:1 or above (or overseas equivalent); and

• Master's degree in a relevant cognate subject normally with an overall average of 65% or above (or equivalent)

• Professional qualifications and/or relevant and appropriate experience.

Desirable Criteria:

• A degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Mathematics, Engineering, Information Systems, or a closely related discipline.

• A Master’s degree in one of the above areas.

• Strong analytical and programming skills (e.g., Python, machine learning frameworks) are advantageous, alongside an interest in applied AI, decision‑making systems, and explainable or uncertainty‑aware modelling.
• Candidates from numerate disciplines with professional experience in data science, analytics, financial technology, investment analysis, or innovation ecosystems are also encouraged.

Crucially, applicants should be motivated to conduct high‑quality research at the intersection of AI and real‑world enterprise applications, with an interest in developing transparent, explainable and user‑centred decision‑support technologies.

English Language Evidence:

• IELTS minimum scores - 7.0 overall, 6.5 other sections. Other tests may be considered.

The application deadline will be 11:59PM (GMT) on 29/05/26.

Apply online for PhD by Enterprise HUMS.

If you would like to discuss the project further, contact Prof Richard Allmendinger (richard.allmendinger@manchester.ac.uk)

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