Evolving cyber-physical-social Systems (eCPSS): integrating research demonstrators into a client-facing MVP MSc by Research

Join Cranfield University's Systems Realization Group (SRG) for an MRes project combining software engineering, data science and human-centred AI. You will integrate existing research demonstrators into a client-facing minimum viable product (MVP), enabling users to explore operational changes, compare responses and review the authority and rationale behind decisions.

Organisations need to adapt their operations as requirements, resources and priorities change. AI and digital tools can support this adaptation, but users also need clear oversight, understandable recommendations and traceable decisions. SRG develops and evaluates principles and mechanisms that enable engineered systems to evolve while preserving safety, accountable human authority and sustainability.

The project will investigate how existing research demonstrators can be integrated into a coherent, usable and traceable decision-support workflow. The four demonstrators address human-AI collaboration, governed requirements evolution, Management of Change through an ontology and knowledge graph, and factory disruption response. The student will assess the existing software, define a modular architecture and shared data interfaces, connect the components through APIs or adapters, and build a client-facing web interface. A bounded manufacturing workflow will provide the initial end-to-end use case; further integration will be prioritised with the supervisors according to feasibility and research value. The research question is how integration and interface design affect task completion, usability and decision traceability while retaining appropriate human approval.

The student will join SRG within the Sustainable Manufacturing Systems Centre (SMSC), Manufacturing, Materials and Design Theme, at Cranfield University, supervised by Dr Jelena Milisavljevic Syed. The project supports the Symmetra Systems Design Studio initiative, which aims to translate existing SRG research into an externally testable offering.

Expected outputs include an integrated MVP with a client-facing interface; documented architecture, data models and component interfaces; reproducible installation and demonstration guidance; and an MRes thesis reporting the design and evaluation. Using agreed change scenarios, the student will evaluate integration reliability, task completion time, usability, and the completeness of approval and decision records. Where feasible and subject to relevant approvals, evaluation will include prospective users or stakeholders.

The student will build on existing prototypes and explore the practical challenge of connecting AI, knowledge-based tools and human oversight in one workflow. The project offers opportunities to engage with SRG collaborators and prospective users across manufacturing and healthcare, subject to availability and relevant approvals. SRG works closely with partnering Systems Realization Labs in the USA and Ireland.

The student will develop skills in software architecture, full-stack development, API and data integration, human-centred design, research evaluation and technical communication.

Entry Requirements:
Applicants should have a first or second class UK honours degree or equivalent in Computer Science or Data Science. Applicants from closely related computing disciplines with equivalent practical experience may also be considered. Strong programming skills are essential, including proficiency in Python or an equivalent language and evidence of building working software. Applicants should be able to work with APIs, structured data, databases and version control, and demonstrate the ability to develop user-facing applications. Experience with JavaScript/TypeScript and web frameworks, automated testing, AI/LLM integration, knowledge graphs or ontology tools, and deployment is desirable. Applicants should be interested in usability and accountable AI, communicate clearly, and work both independently and collaboratively. Prior manufacturing or healthcare experience is not required.

Funding:
This project is self-funded. No stipend or fee waiver is offered. Applicants or their sponsors must provide financial support for tuition fees, any applicable research support fees and living expenses.