Bournemouth University PhD Studentship – Automated Risk Management in the UK
PhD Studentship – Automated Risk Management
Between the climate crisis, political tension and other rising threats, the era of cascading risk across and between enterprises is here to stay. To survive in this new era, governments and businesses will need the capability to identify early indicators ahead of risk events, and to act proactively to mitigate those risks before they become disruptions. Today, enabling these capabilities will require a risk management program that blends data science, automation and Artificial Intelligence (AI).
This PhD project will consider different approaches to automation (e.g. rules-based, expert systems, recommender systems, machine learning), and assess the role they could play in risk management systems. It will also consider how risk appetite can be elucidated from an operator and used to modify and automated processes in real-time.
A key outcome from the project will be to understand/demonstrate what the appropriate level of automation/cognition is for risk management systems, and how it can be achieved. A novel aspect of this research is the automation of risk management when risk appetite, risk tolerance, and risk thresholds are constantly changing, as occurs in fast-paced military operations.
Many systems exist as part of a system of systems, and so do not entirely operate in isolation. Thus, later stages of this project will consider automated risk management in a system of systems enterprise risk management context, which is another novel aspect of this research.
Building on the successful collaboration between Bournemouth University and Defence Science Technology Labs (Dstl), this joint PhD project aims to develop an evidence-based approach, supported by a prototype toolkit to enhance risk management through automation by considering different approaches to automation.
This project offers a very interesting learning and training opportunity for the candidate. Different subjects are involved in the project such as risk management, systems engineering, human-machine interaction, machine learning, cyber security, etc.
This is a fully-funded PhD studentship which includes a stipend of £15,921 each year to support your living costs.
Key information
Next start date:
24 April 2023
Location:
Bournemouth University, Talbot Campus
Duration:
36 months
Entry requirements:
Outstanding academic potential as measured normally by either a 1st class honours degree or equivalent Grade Point Average (GPA), or a Master’s degree with distinction or equivalent. If English is not your first language you’ll need IELTS (Academic) score of 6.5 minimum (with a minimum 6.0 in each component, or equivalent). For more information check out our full entry requirements.
Project details
Between the climate crisis, political tension and other rising threats, the era of cascading risk across and between enterprises is here to stay. To survive in this new era, governments and businesses will need the capability to identify early indicators ahead of risk events, and to act proactively to mitigate those risks before they become disruptions. Today, enabling these capabilities will require a risk management program that blends data science, automation and Artificial Intelligence (AI).
This PhD project will consider different approaches to automation (e.g. rules-based, expert systems, recommender systems, machine learning), and assess the role they could play in risk management systems. It will also consider how risk appetite can be elucidated from an operator and used to modify and automated processes in real-time.
A key outcome from the project will be to understand/demonstrate what the appropriate level of automation/cognition is for risk management systems, and how it can be achieved. A novel aspect of this research is the automation of risk management when risk appetite, risk tolerance, and risk thresholds are constantly changing, as occurs in fast-paced military operations.
Many systems exist as part of a system of systems, and so do not entirely operate in isolation. Thus, later stages of this project will consider automated risk management in a system of systems enterprise risk management context, which is another novel aspect of this research.
Building on the successful collaboration between Bournemouth University and Defence Science Technology Labs (Dstl), this joint PhD project aims to develop an evidence-based approach, supported by a prototype toolkit to enhance risk management through automation by considering different approaches to automation.
View the full project description (pdf 271kb)
The closing date for applications is 31 January 2023.