About the Project
We are seeking a PhD student to work in a collaborative research project between the National Centre for Earth Observation (NCEO, www.nceo.ac.uk/), University of Reading (UoR) and Plymouth Marine Laboratory (PML). The project will address the timely question about marine ecosystem resilience in the face of human pressures and climate change. The student will use complex networks (Boccaletti et al, 2006) to understand the pathways through which the anthropogenic signal propagates across ecosystem variables and geographic regions within the ERSEM ecosystem model of the North-West European Shelf (NWES), a key region for European food security (Legge et al, 2020). The aim is to identify variables and sub-regions having the largest impact on the NWES marine ecosystem, thus providing insight into the ecosystem vulnerability. This will deliver crucial information on which model degrees of freedom are redundant, suggesting how to reduce complexity (and the cost) of the ecosystem model. The student will construct a low-complexity emulator of the ecosystem model using machine learning and explore its incorporation within the NWES data assimilation system.
This is an interdisciplinary project at the interface of mathematics and environmental science whereby complex networks and machine learning are combined to deliver new profound insights into marine ecosystems.
The student will be enrolled at the Dept of Meteorology of UoR within the Data Assimilation Research Centre (https://research.reading.ac.uk/metdarc). In the first year, the student will be offered the opportunity to be based at PML, a centre of excellence for marine science and marine ecosystem modelling. She/he will be also affiliated to the NCEO, a centre of over 100 scientists in the UK. The student will benefit from training at UoR, PML and NCEO.
Eligibility requirements:- We expect you to have a 1st or upper 2nd class degree, or a master's with Distinction or Merit, in mathematics, physics, computer science, or environmental sciences.
Legge et al., Frontiers in Marine Science 7, (2020):143.
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