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- Tasks: Conduct groundbreaking research in AI and decision-making for investment and enterprise.
- Company: The University of Manchester, a top 5 UK business school.
- Benefits: Fully funded studentship covering tuition fees and a stipend of £21,805 per annum.
- Other info: Exciting opportunity to impact real-world innovation ecosystems and develop explainable AI systems.
- Why this job: Join a pioneering PhD programme that merges research with entrepreneurship training.
- Qualifications: Bachelor's and Master's degrees in relevant fields; strong analytical skills preferred.
The predicted salary is between 21805 - 21805 £ per year.
Qualification Type: PhD
Location: Manchester - UK
Funding for: UK and International
Funding amount: £21,805 per annum
Start date: September 2026
Hours: Full Time
Closes: 29 May 2026 (midnight)
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 environments, such as venture capital, private equity, and university innovation ecosystems, are becoming increasingly data intensive. Yet despite the abundance of available information, decision-making across deal sourcing, evaluation, due diligence, and post investment monitoring remains fragmented and highly manual. Current commercial platforms excel at search and data aggregation, but they provide limited support for deeper reasoning, scenario exploration, or coordinated, lifecycle wide decision support.
This PhD project, AIDE: Agentic Intelligence for Decision-making in Investment and Enterprise, aims to address these challenges by developing next-generation AI systems capable of supporting holistic, data-driven and uncertainty-aware decision-making. Based in the prestigious Alliance Manchester Business School, the project will also explore the design and development of knowledge graphs to structure and connect heterogeneous data sources, enabling richer contextual understanding and reasoning.
The project offers an exciting opportunity to work at the frontier of applied AI, decision sciences, and real-world innovation ecosystems, advancing new research while contributing to a potential future commercial venture. A central ambition of the project is to build AI systems that are not only powerful, but also explainable. Investment decisions are high-stakes, and users must be able to understand why the system recommends particular actions or highlights certain risks.
The PhD will explore explainable AI (XAI) methods that enable transparency, interpretability and user trust, ensuring that recommendations can be interrogated, justified, and adapted by human experts. This includes surfacing the 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.
The research will investigate how diverse information sources, such as structured financial data, textual documents, company disclosures, and online signals, can be integrated into unified representations that support robust reasoning, including the construction and utilisation of knowledge graphs for entity linking, relationship modelling, and semantic integration. Equally important is modelling uncertainty: decision-makers often work with incomplete, noisy or fast-changing data. The project will examine techniques for quantifying and propagating uncertainty across multi-stage workflows, enabling users to explore how assumptions or market changes affect potential outcomes.
The student will also study 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 potentially serving as a shared structured memory and coordination layer across agents. The design will emphasise human-AI collaboration, ensuring users retain oversight, agency, and the ability to challenge or override recommendations.
Methodologically, the project blends machine learning, probabilistic modelling, multi-agent systems, explainable AI, and human-computer interaction, alongside knowledge representation and graph-based reasoning techniques. A design-science research approach will be used, with iterative prototyping, evaluation using realistic scenarios, and engagements with practitioners from investment and innovation communities.
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. TOEFL (internet based) test minimum scores - 100 overall, 25 in all sections. Pearson Test of English (PTE) UKVI/SELT or PTE Academic minimum scores - 76 overall, 76 in writing, 70 in other sections. To demonstrate that you have taken an undergraduate or postgraduate degree in a majority English speaking nation within the last 5 years. 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.
PhD by Enterprise employer: The University of Manchester
Contact Detail:
The University of Manchester Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land PhD by Enterprise
✨Tip Number 1
Network like a pro! Reach out to current PhD students or alumni from the programme. They can give you insider info on what to expect and how to stand out during interviews.
✨Tip Number 2
Prepare for your interview by diving deep into the project details. Understand the key challenges and think about how your skills can contribute to solving them. Show us you’re not just interested, but passionate!
✨Tip Number 3
Practice your pitch! You’ll want to clearly articulate your research interests and how they align with the AIDE project. Keep it concise and engaging – we love enthusiasm!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, it shows you’re serious about joining our community.
We think you need these skills to ace PhD by Enterprise
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your application to highlight how your skills and experiences align with the PhD project. We want to see your passion for AI and decision-making, so don’t hold back on showcasing relevant projects or coursework!
Showcase Your Research Interests: In your written application, clearly express your research interests and how they connect to the AIDE project. We’re looking for candidates who are genuinely excited about exploring explainable AI and its applications in investment decision-making.
Be Clear and Concise: Keep your writing clear and to the point. We appreciate well-structured applications that are easy to read. Avoid jargon unless it’s necessary, and make sure your enthusiasm shines through!
Apply Through Our Website: Don’t forget to submit your application through our official website! It’s the best way to ensure your application is received and considered. Plus, you’ll find all the details you need right there.
How to prepare for a job interview at The University of Manchester
✨Know Your Research
Make sure you’re well-versed in the specifics of the AIDE project and its goals. Familiarise yourself with concepts like explainable AI, knowledge graphs, and decision-making systems. This will not only show your genuine interest but also help you engage in meaningful discussions during the interview.
✨Showcase Your Skills
Highlight your analytical and programming skills, especially in Python and machine learning frameworks. Be ready to discuss any relevant projects or experiences that demonstrate your ability to work with data-intensive environments and your understanding of investment analysis.
✨Prepare Questions
Think of insightful questions to ask about the PhD programme and the research environment at Alliance Manchester Business School. This shows your enthusiasm and helps you assess if the programme aligns with your career goals. Ask about collaboration opportunities and how the research impacts real-world applications.
✨Demonstrate Your Motivation
Express your passion for conducting high-quality research at the intersection of AI and enterprise applications. Share your vision for how you can contribute to the project and why you’re excited about developing transparent and user-centred decision support technologies.