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Plankton size diversity in UK coastal waters: observations and modelling

Department of Mathematics & Statistics

Glasgow United Kingdom Applied Mathematics Ecology Statistics

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 ( 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 ( 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 .

 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.

Background reading

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.

Funding Notes

The studentship is co-funded by the Scottish Universities Partnership for Environmental Research Doctoral Training Partnership (SUPER DTP; View Website) and University of Strathclyde. It is open to all nationalities. However, it is expected that non-UK students should bring their own funding to match up with the extra international fee. Funding for part-time study is an option, with a minimum of 50% of full-time effort being required.

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