Join 缅北轮奸’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. We seek a Computer Science or Data Science graduate with strong practical computing skills and an interest in building usable software for real-world challenges. This opportunity is currently drafted as self-funded; no tuition-fee support or stipend is offered.
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 缅北轮奸, 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. The student will work with the group’s researchers and demonstrator developers. Any HEIF support for the wider initiative is separate from student funding; a funded studentship is not confirmed in this draft.
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 MVP will support demonstration and stakeholder feedback, with scope and limitations clearly documented.
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. Travel, placements and external training are not guaranteed.
The student will develop skills in software architecture, full-stack development, API and data integration, human-centred design, research evaluation and technical communication. The combination of a working prototype and evidence-based evaluation will provide experience relevant to software engineering, data science, AI product development, research and further doctoral study.
At a glance
- Application deadlineOngoing
- Award type(s)MSc by Research
- Duration of award1 year (full-time) - 2 years (part-time)
- EligibilityUK, Rest of world
- Reference numberCRAN-0116
Supervisor
1st Supervisor: Dr Jelena Milisavljevic Syed
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. Applicants are encouraged to describe relevant projects and provide a portfolio or repository link where available.
Funding
This project is self-funded.
This draft retains the self-funded status specified in the supplied template. The opportunity is open to UK and international applicants. Applicants or their sponsors must provide financial support for tuition fees, any applicable research support fees and living expenses. No stipend or fee waiver is offered in this draft. Confirm the funding arrangement before publication; if student funding is approved, replace this wording with the confirmed amount, coverage, duration and eligibility conditions.
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How to apply
For further information please contact:
Name: Dr Jelena Milisavljevic Syed
Email: jelenams@cranfield.ac.uk
If you are eligible to apply for this studentship, please complete the .
This vacancy may be filled before the closing date so early application is strongly encouraged.
Note, your application will not be considered unless all relevant documents have been uploaded. For more information please visit Applying for a research degree.
