Research Associate in Complex Dynamic Weather Processes Modelling Using Interpretable Machine Learning and AI Techniques

We have an exciting opportunity in the Department of Automatic Control and Systems Engineering (ACSE). The position is funded through the NERC highlight topic project PICANTE (Processes, Impacts, and Changes of ANTarctic Extreme weather), led by the Department of Geography at TUoS, involving a number of partner institutions.

You will have a unique opportunity and platform to research and develop interpretable machine learning and AI methods for better understanding the complex dynamic weather processes leading to extreme weather events in the Antarctic.

Obtaining improved projections of how AEWE and their Antarctic climate and ice may be affected by climate change is crucially important. We aim to transform understanding of the characteristics and drivers of Antarctic extreme weather events (AEWE), disentangle the roles of human influence and natural climate variability in changes to AEWE and their drivers, and to use this knowledge to predict future events and their impacts on Antarctic climate and ice. AEWE have global implications, through potential detrimental impacts on ice shelf stability, leading to ice mass loss and global sea level rise.

You will use interpretable machine learning and AI techniques, including NARMAX methods together with other nonlinear system identification approaches to understand the large-scale and/or remote meteorological drivers of AEWE.  You will work closely with other members of the project team to advance the understanding of the relative contributions of the chain of drivers of these events across different scales, and of their local meteorological impacts, and apply these understandings to assess the performance of state-of-the-art climate models.

You should hold a PhD (or be close to completion) in a closely related area, with strong expertise and high skills in data-driven modelling, nonlinear system identification, interpretable machine learning, and AI techniques used for multi-modal and multivariate data modelling.

ACSE is a world-leading research department, as evidenced by the results of the 2021 Research Excellence Framework (REF2021) exercise.  We are proud to have come 8th in the REF 2021 in terms of the quality of our research.  96% of our research is rated in the highest two categories in the REF 2021, meaning it is classed as world-leading or internationally excellent.

The line manager for this post is Dr. Hua-Liang Wei, though you will also benefit from the expertise of Dr Sihan Li and Dr Julie Jones from the Department of Geography.

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