Risk assessment in synchronous and asynchronous complex business networks
Applications are now CLOSED
This proposed research aligns to the new Advanced Research and Engineering centre (ARC) within Northern Ireland. This Centre will drive future innovations in technology and enhance our capabilities in important research areas such as robotic process automation (RPA), workflow automation, visualisation, data analytics and artificial intelligence (AI). The Centre brings together expertise from PwC, University of Ulster, and Queen’s University Belfast. This research project aligns to the workflow and AI streams within the Centre. A selection process will determine the strongest candidates across a range of projects, who may then be offered funding for their chosen project. This project is expected to explore operable business processes over a complex network covering multiple data sources. Depending on the type of data, the complexity of the porting software, and the dependencies between the data, these sources of data may have synchronous, asynchronous, or both characteristics. The business networks make decisions based on these inputs and are susceptible to error due to the data's multiple interconnections. As a result of the associated complexities and interdependence of the data points, several risky decision-making factors are frequently overlooked when a model of operations is so intricate. In a broader sense, failure to identify risk can result in inputs remaining undiagnosed for a longer period, leading to significant and more pervasive deficiencies over time. Thus, it is desired to develop methods to comprehend these entanglements of data from multiple sources in synchronous or asynchronous business networks and to provide solutions to flag the potential risks. In addition, reducing false positives without compromising the accuracy of risk mapping is an additional challenge that must be addressed as part of this project.
Business networks with multiple data points are challenging to map and profile for risks. This project is anticipated to investigate executable business processes over a complex network containing various synchronous and asynchronous data sources. The project will expand the understanding of the risk landscape and generate valid methods that can replace the existing clause approaches with more comprehensive and novel AI-assisted methods that can dynamically adjust the criteria without affecting the system's accuracy. The project will explore business processes and networks from multiple directions – link analysis, relationships between data and suspicious workflow, security risks, to name a few. Such provisioning will ensure that business decisions are taken based on true data by considering potential risks associated with the processes.
The project aims to develop methods of defining checks and clauses with the assistance of computational game theory to reduce the false positives in the risk flagging system and introduce potential reasoning capabilities within the mappings for synchronous and asynchronous business networks that support coordinated decision-making.
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*Please note that the deadline for applications from international candidates closed on 28 February*
International: We welcome applications from international candidates. For candidates who do not meet the DfE funding residency requirements, a small number of international studentships may be available from the School. These are awarded via a competitive, selection process which will determine the strongest candidates across a range of School projects, who may then be offered funding for their chosen project.
UK/ROI: Applications from candidates in the UK and ROI are eligible for consideration for a DfE Studentship. As this is an industry-sponsored PhD, approximately £6,000 per year is payable to the sponsored student in addition to the annual DfE stipend if successful. Full eligibility information for UK/ROI candidates can be viewed via: https://www.economy-ni.gov.uk/publications/student-finance-postgraduate-studentships-terms-and-conditions
A 1st or 2.1 Hons degree or MSc with distinction in Electrical/Electronic Engineering or Computer Science with relevant technological experience.