Bournemouth University PhD Studentship – Automated Risk Management in UK

PhD Studentship – Automated Risk Management

Between the climate crisis, political tensions and other rising threats, the cycle of increased risk among and between businesses is here to stay. To survive in this new era, governments and businesses will need the ability to identify early indicators of risk events and act proactively to mitigate those risks before they become disruptive. Today, enabling these capabilities requires risk management software that integrates data science, automation, and Artificial Intelligence (AI).

This PhD project will review different approaches to automation (e.g. rule-based, expert systems, recommender systems, machine learning) and evaluate the role they can play in risk management systems. It will also consider how risk appetite can be clarified by the operator and used to change and automate processes in real-time.

The main outcome of 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 in fast-moving military operations.

Many systems exist as part of a system of systems and therefore do not operate in complete isolation. Thus, in the later stages of this project, automated risk management in a system of systems will be considered in the context of enterprise risk management, another new aspect of this research.

Building on the successful collaboration between Bournemouth University and the Defense Science Technology Laboratories (Dstl), this joint PhD project aims to develop an evidence-based approach supported by a prototype toolkit to improve risk management through automation, taking into account different approaches to automation.

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This project provides a very interesting learning and training opportunity for the candidate. The project includes risk management, systems engineering, human-machine interaction, machine learning, cyber security, and more. such as various topics are involved.

This is a fully funded PhD studentship consisting of a stipend of £15,921 per year to support your living costs.

Basic information

Next start date:

April 24, 2023

Location:

Bournemouth University, Talbot Campus

Duration:

36 months

Entry Requirements:

Outstanding academic potential, normally measured by either a 1st class honors degree or equivalent Grade Point Average (GPA) or a Master’s degree with honors or equivalent. If English is not your first language, you need a minimum IELTS (Academic) score of 6.5 (minimum 6.0 in each component or equivalent). Check our full entry requirements for more information.

Project details

Between the climate crisis, political tensions and other rising threats, the cycle of increased risk among and between businesses is here to stay. To survive in this new era, governments and businesses will need the ability to identify early indicators of risk events and act proactively to mitigate those risks before they become disruptive. Today, enabling these capabilities requires risk management software that integrates data science, automation, and Artificial Intelligence (AI).

This PhD project will review different approaches to automation (e.g. rule-based, expert systems, recommender systems, machine learning) and evaluate the role they can play in risk management systems. It will also consider how risk appetite can be clarified by the operator and used to change and automate processes in real-time.

The main outcome of 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 in fast-moving military operations.

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Many systems exist as part of a system of systems and therefore do not operate in complete isolation. Thus, in the later stages of this project, automated risk management in a system of systems will be considered in the context of enterprise risk management, another new aspect of this research.

Building on the successful collaboration between Bournemouth University and the Defense Science Technology Laboratories (Dstl), this joint PhD project aims to develop an evidence-based approach supported by a prototype toolkit to improve risk management through automation, taking into account different approaches to automation.

See full project description (pdf 271kb)

The application deadline is January 31, 2023.

Official site

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