Research Associate (606239)

Strathclyde Business School

Term: Fixed Term (18 months)

The successful applicant will join the Department of Management Science in the University of Strathclyde and work with experts in Nuclear Decommissioning Authority (NDA). Through this collaborative project, we will develop and evaluate a method that combines engineering judgment with historical data for project cost modelling.

This research addresses two well-documented problems that produce poorly calibrated probabilities for project cost modelling: optimism bias and failure to recognise dependency between uncertainties. Calibrated measurements of the probability of uncertain events, i.e. consistent with the frequency of events over a portfolio of projects, is vital for risk management.  Optimism bias is the systematic tendency for project appraisers being overly optimistic, resulting in a higher frequency of unfavourable consequences than anticipated. Naively assuming independence when aggregating risks underestimates uncertainty. These two problems result in over confidence that favourable outcomes will be achieved.

In this project, we will develop and evaluate a framework that combines engineering judgment with historical data. We will draw on the richness of engineering judgement to identify and assess dependencies through elaborating on risks that are specific to the project and combine with data from a variety of industries to transfer learning and support statistical based assessments.

The outputs of this research will be a generic framework to forecast project costs, measure the uncertainty of events being realised, inform risk mitigation strategies, monitor project outcomes, and update risk assessment in light of new data through the project lifetime. We will evaluate this process on industrial case studies.

As a Research Associate, under the general guidance of a research leader, you will play a lead role in relation to this project contributing to the development of new methods for analysis. You will write up research work for publication, individually or in collaboration with colleagues, and disseminate the results via peer reviewed journal publications and presentation at conferences. You will join external networks to share information and ideas, collaborate with colleagues to ensure that research advances, participating in initiatives, which establish research links with industry.

To be considered for the role, you will be educated to a minimum of PhD level in an appropriate discipline or have significant relevant experience in addition to a relevant degree. You will have sufficient breadth or depth of knowledge in data analytic methods and a developing ability to conduct individual research work and to disseminate results. You will have an ability to plan and organise your own workload effectively and an ability to work within a team environment. You will have excellent interpersonal and communication skills, with the ability to listen, engage and persuade, and to present complex information in an accessible way to a range of audiences.

Whilst not essential for the role, applications are welcomed from candidates with relevant work experience, membership of relevant Chartered/Professional bodies (including the Higher Education Academy), experience of relevant student supervision and teaching activities, and/or experience of knowledge exchange related activities.

Formal interviews for this post will be held on Monday, 10 June 2024.

Informal enquiries about the post can be directed to Professor John Quigley, j.quigley@strath.ac.uk.

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