This is a self-funded PhD position to work with Dr Maryam Farsi in the Centre for Digital Engineering and Manufacturing. This PhD project will focus on human-machine interaction for decision-making. The project aims to enhance the robustness, flexibility and automation of such collaboration using advanced machine learning, artificial intelligence and uncertainty quantification approaches. The potential outcome of this research will tackle the challenges and limitations of interactive human-machine or human-AI decision-making. The goal is to find a more confident, predictable and adaptive solution for human-AI decision-makings. Read more Read less
This PhD project aims to quantify the robustness and adaptiveness of decisions that human takes when co-using AI in decision making. In this regard, the quality of data and information and level of intelligence and smartness of AI is critical. This PhD will bring together several research themes in the field of decision-making, AI, human-machine, complex systems simulation, and deep learning modelling techniques.
1. Developing a dynamic and stochastic model of natural human intelligence function in decision making
2. Developing the dynamic and stochastic model of an AI function in decision making
3. Evaluating the integrated confidence in decision making when human use AI
4. Quantifying the uncertainty in data and information uses in decision making when human use AI
5. Quantifying the robustness in decisions human make when using AI
6. Predicting the point of lack of trust and robustness in decisions human make when using AI
At Cranfield, the candidate will be based at the Centre for Digital Engineering and Manufacturing which hosts cutting-edge digital engineering facilities. The student will have access to high-end computers for simulating the complex nature of maintenance. The candidate works on his/her research individually and collaborates with other researchers in the field at the Centre
At a glance
- Application deadlineOngoing
- Award type(s)PhD
- Duration of award3 years
- EligibilityUK, EU, Rest of World
- Reference numberSATM204
Entry requirementsCandidates should have a minimum of an upper second (2.1) honours degree (or equivalent) preferably in Computer Science/ Mechanical Engineering / Industrial Engineering / Mathematics / Operations Research but candidates in other degrees related to Engineering or related quantitative fields would be considered. Candidates with an MSc degree in these disciplines will be desirable.
FundingThis is a self-funded PhD; open to UK, EU and International applicants.
About the sponsorThis is a self-funded PhD that includes the ability to participate in industry-led research initiatives and access to the Cranfield Doctoral Training Network.
Cranfield Doctoral Network
Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This network brings together both research students and staff, providing a platform for our researchers to share ideas and collaborate in a multi-disciplinary environment. It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.