Research Fellow in Decision Making under Deep Uncertainty

The School of Earth and Environment at the University of Leeds invites applications for a postdoctoral research fellow in Decision Making under Deep Uncertainty

You will participate in a six-year EPSRC-funded research project, Multi-Actor Adaptive Decision Making (MAADM). MAADM aims to understand how to make better decisions to transform infrastructure systems, taking into account deep physical and social uncertainties and the fact that multiple actors must make decisions and interact to deliver system transformation. A core part of the MAADM project is an in-depth case study, working with project partners to apply and develop adaptive decision-making approaches and modify partner appraisal processes. Under this post, you will test and refine decision making under deep uncertainty (DMDU) approaches (such as dynamic adaptive decision policy pathways and robust decision making), with a real case study in regional transport planning.

You will have a PhD or near completion in decision making under deep uncertainty. Any additional experience in transport sector modelling would be an advantage. You will also have experience of working with stakeholders to co-produce knowledge. Additionally, to work effectively in a collaborative team environment, you should have excellent communication and interpersonal skills. The role would suit an individual with interest in applying DMDU techniques to sustainable transport planning.

This post is funded full-time for 18 months but we would encourage flexible and part-time working arrangements stretching over a longer period. All other staff on the project work part-time.

To explore the post further or for any queries you may have, please contact:

Dr Katy Roelich, Associate Professor in Decision Making under Deep Uncertainty


Deadline for applications is 14th August 2020. Please apply via the University of Leeds Job Portal


Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.