PhD Studentship: Complex Network Approach to Improve Marine Ecosystem Modelling and Data Assimilation
University of Reading
Nonlinear Processes in Geosciences (NP)
Ocean Sciences (OS)
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.
Boccaletti et al., Physics Reports 424.4-5, (2006):175-308.
Legge et al., Frontiers in Marine Science 7, (2020):143.
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.
Desired knowledge: complex networks, machine learning, marine ecology.
This opportunity is open to candidates worldwide but only covers Tuition Fees at the UKRI rate. A successful international candidate would need to fund or secure sponsorship for the difference in the fees (approximately £16,330/year).
3 years award
Funding covers full tuition fees plus UKRI stipend
How to apply:
Click at https://www.risisweb.reading.ac.uk/si/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=P_ADM&code2=0001, create your account and use the link sent by email to start the application process. During the application process select the PhD in Atmosphere, Oceans and Climate, and upload transcripts, CV, certificates and in the “other’s” the motivation letter and the contacts of up to three referees.