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PhD Studentship in Workflow modelling and analysis of smart contracts

Project Description

Overview of the Project
Smart contracts provide the means for streamlined collaboration, where the interactions, administration, and data flows are managed by computer agents in a trustworthy and transparent way, allowing humans to focus more on problem solving and value generation. As smart contract technologies are increasingly adopted by small and large organisations, the need to provide rigorous methodologies for their development, analysis, and validation is becoming a priority. Formal workflows can enable the modelling of smart contracts in a way that is unambiguous and understandable, yet amenable to systematic analysis and deployment.
The aim of the project is to research workflow-based methodologies and tools for the next generation of systems underlying machine-mediated collaboration within and across organisations.

A successful candidate will be expected to develop and use novel symbolic Artificial Intelligence and data-driven techniques to innovate across the stages of the smart contract lifecycle, for instance in terms of intuitive design, collaborative modelling, rigorous validation, automated deployment, and adaptive monitoring.
The candidate will also have the opportunity to apply their research in real-world problems, in collaboration with stakeholders from the industry and public sectors. Examples of application areas include but are not limited to:
• Patient pathways for health and social care
• Manufacturing flows for Industry 4.0
• Business process and supply chain management
• Social machines

This research will be enabled through interaction with the University of Edinburgh’s Data-Driven Innovation hubs, namely the Bayes Centre (digital technologies), the Usher Institute (health and social care) and the Edinburgh Futures Institute (creative industries, public sector, fintech), as well as the Business School.

Candidate’s profile
• A Masters degree with Distinction (or a First Class honours BSc in exceptional cases) from a leading UK university or equivalent overseas/professional qualification in computer science, artificial intelligence, mathematics, or other related subject.
• Strong analytical and object-oriented and/or functional programming skills.
• Knowledge or experience in at least one field of symbolic AI (such as automated reasoning, multi-agent systems, automated planning, etc.).
• Good understanding or knowledge of at least two of the following: process algebras, workflow management, business process management, agent-based simulation, diagrammatic reasoning, theorem proving, model checking, parallel/distributed computing, principles of programming languages (especially concurrency), process mining
• Keen and demonstrable interest in at least one of the application areas is also desirable.

Funding Notes

The studentship covers:
• Full time PhD tuition fees for a student with UK/EU nationality (£4,327 per annum, subject to annual increment).
• A tax-free stipend of GBP £15,009 per year for 3 years.
• Additional programme costs of £1000 per year.

Related Subjects

How good is research at University of Edinburgh in Computer Science and Informatics?

FTE Category A staff submitted: 94.85

Research output data provided by the Research Excellence Framework (REF)

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