About the Project
We are looking for a highly motivated, numerate student with an interest in marine plankton ecology and mathematical modelling to join our group. This fully funded 3.5-year studentship needs to be filled by 30 April 2021, so we recommend applying immediately.
Plankton are critically important for marine habitats supporting their food webs and influencing ecosystem health. They vary enormously in size – from the size of a bacterium to being visible to a naked eye. Size is one of the most important traits in plankton, determining their growth, respiration, resource uptake and vulnerability to predation. Plankton size also determines how much food is available to upper trophic levels such as fish. Therefore, it is crucial for us to understand what controls plankton size structure in the ocean. The environmental controls on plankton mean size have been extensively studied, but much less is known about what affects size diversity. The successful candidate will: 1) have the opportunity to tackle this problem by taking advantage of the long-term observational data in UK coastal waters; 2) build state-of-art plankton models and use the observational data to optimize these models; and 3) apply the plankton size-based models to answer questions regarding planktonic food-webs and trophic interactions in context of climate change.
It is anticipated that you will receive substantial training in mathematical and statistical modelling including but not limited to analyses of ordinary and partial differential equations and Bayesian inference. You will also have the opportunity to use the high-performance computing system in Strathclyde (https://www.archie-west.ac.uk/). Your mathematical, statistical, and programming skills are expected to be substantially enhanced during the PhD training. These skills will be very useful for securing some of the most popular jobs in this Big Data era. You will also gain a deep understanding of the ecology of biodiversity and coastal oceanography, which is essential for protecting our planet.
You will mainly work within the Marine Population Modelling group, Department of Mathematics and Statistics, University of Strathclyde (https://www.strath.ac.uk/science/mathematicsstatistics/smart/marineresourcemodelling/). You will also have the opportunity to collaborate with scientists in Scottish Association for Marine Science (SAMS) and Marine Scotland Science (MSS).
Applicants should have or expect to obtain a good honours degree (1, 2.1, or equivalent) in ecology, oceanography, applied mathematics, statistics, or a highly quantitative science. A highly quantitative background and experience of numerical modelling is desirable. Experience programming in R, Fortran, C/C++, Python, or Matlab would be highly beneficial, but not essential.
How to apply
To apply, send 1) a complete CV, 2) a 1 page personal statement explaining your interests and skills for this project, and 3) names and contact information of three references. In your personal statement please describe i) your interests and skills for this project, ii) your ability to critically analyze different sources of information and data, and iii) your motivation and desired training. Application materials are to be submitted to the lead supervisor, Dr Bingzhang Chen, Department of Mathematics and Statistics, University of Strathclyde, Glasgow at firstname.lastname@example.org.
We value diversity and welcome applications from all sections of the community.
The University currently holds a Bronze Athena SWAN award, recognising our commitment to advancing women’s careers in science, technology, engineering, maths and medicine (STEMM) employment in academia.
Key Information and Funding Notes
The project will start immediately, but with an official start date of 27th September 2021 and an induction event in Glasgow on 4th October 2021.
The student will be enrolled in the SUPER Graduate School and onto the SUPER Post Graduate Certificate in Researcher Professional Development.
Acevedo-Trejos, E. et al. (2018). Proc. R. Soc. B, 285: 20180621.
Anderson, T.R., Gentleman, W.C. and Yool, A. (2015). Geosci. Mod. Dev., 8, 2231-2262.
Chen, B., Smith, S.L., & Wirtz, K. (2019). Ecol. Lett., 22, 56-66.
Haario, H., et al. (2006). Stat. Comp., 16, 339-354.
Schmidt, K., et al., (2020). Global Change Biol., 00:1-14.
Ward, B.A. et al. (2012). Limnol. Oceanogr., 57, 1877-1891.
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