About the Project
Complex networks (e.g. Boccaletti et al., 2006) are a highly efficient mathematical approach to represent connectivity between the degrees of freedom within a diverse range of complex phenomena: from trophic relationships in ecology to representing climate through a network of teleconnected local oscillators, all the way to the financial markets, social networks and the web, neuroscience, epidemic modelling, Ising model, transportation, security and further (for an overview see Costa et al., 2011). We propose here to use complex
networks to understand the pathways through which the antrophogenic signal propagates on the NWES, both between different ecosystem variables and between different geographical regions. This will enable us to identify the key variables and the NWES sub-regions having the largest impact on the NWES marine ecosystem dynamics. Such analysis will provide insight into the ecosystem vulnerability, or resilience, however it will also deliver crucial information on which model degrees of freedom are redundant, providing a guide on how to reduce the complexity of the NWES ecosystem model. On this basis we will construct a low-complexity model emulator, relying upon state-of-the-art machine learning tools. In particular the emulator will be designed to improve our current NWES data assimilation system, used to combine marine ecosystem observations with the model forecasts to produce the best possible estimate of the ecosystem state.
This is an interdisciplinary project at the interface of mathematics and environmental science, in which the fast evolving and timely mathematics of complex networks and machine learning methods are combined to deliver new profound insights into marine ecosystems in the shelf seas around the UK. The project can potentially lead to a step-change in the state-of-the-art modelling and data assimilation tools used to represent complex marine ecosystems on the NWES.
The student will be partly placed at Plymouth Marine Laboratory (PML), a centre of excellence for marine science and marine ecosystem modelling, where they will be trained in complex networks, NWES ecosystem processes and the NWES ecosystem numerical model. They will also receive training in programming, data processing and machine learning. PML is participating in a MARINT COST proposal, which, if granted, will offer opportunities to participate in machine learning/emulator building workshops, summer schools and training events. PML co-supervisors: Jozef Skakala and Stefano Ciavatta.
Through the National Centre for Earth Observation (NCEO) the student will have access to the large portfolio of the NCEO training courses and attend its annual conference and young researcher’s forum.
At the University of Reading the student will be exposed to a thriving academic environment and attend training on dynamical systems and data assimilation. There will be opportunities to attend international summer or winter schools on data assimilation and machine learning and virtual training organized by collaborators in Europe and USA. The student may attend conferences in geosciences well as machine learning.
Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree, Masters Degree with Merit, or equivalent in a quantitative discipline, ideally mathematics, physics, computer science, or engineering. Some understanding of oceanography is a bonus. The student should be confident with developing and editing computer codes (e.g. python, R, Fortran), and be able to work in a group and towards project deadlines.
To apply, please follow the instructions at https://research.reading.ac.uk/scenario/apply/
Co-funding from the National Centre for Earth Observation tbc.
Due to UKRI rules, the DTP can only fund a very limited number of international students. We will only consider applications from international students with an outstanding academic background placing them in the top 10% of their cohort.
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