Postgraduate research opportunities

Modelling the life history and dispersal of the alien seaweed Sargassum muticum in Scottish coastal waters

In this project, you will meet with the challenge of developing and testing an individual based model to simulate the life history and dispersal of Sargassum muticum driven by coastal ocean currents.

Number of places

1

Funding

Stipend, Home fee

Opens

13 March 2019

Duration

42 months

Eligibility

This fully funded studentship is available for UK students and EU students who meet the RCUK eligibility criteria. The student will be enrolled in the SUPER Graduate School and onto the SUPER Post Graduate Certificate in Researcher Professional Development. To be eligible for a full award (stipend and fees), a student must satisfy all of these conditions:

  1. Settled status in the UK, meaning they have no restrictions on how long they can stay.
  2. Been ‘ordinarily resident’ in the UK for three years prior to the start of the grant. This means they must have been normally residing in the UK (apart from temporary or occasional absences)
  3. Not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK or EU nationals.)

 

Project Details

This fully-funded 3.5 year studentship will remain open only until filled, but the preferred interview time is in middle March. So we recommend applying immediately.

In this project, you will meet with the challenge of developing and testing an individual based model to simulate the life history and dispersal of Sargassum muticum driven by coastal ocean currents. The alien seaweed Sargassum muticum has been identified as one of the top invasive species threatening Scottish coastal environments. The project mainly consists of two model components: a biological part (life history model) and a physical part (particle tracking model). The life history model will involve solving a system of Ordinary Differential Equations (ODEs), while the particle tracking model will need to solve the stochastic trajectories of particles using either the Fokker-Planck equation or Markov models. The ocean currents will be derived from a state-of-art unstructured grid Finite Volume Community Ocean Model (FVCOM) specifically targeted to Scottish coastal seas. The model outputs will be fitted against field observational data on Sargassum muticum and the parameters will be optimised by Bayesian inference such as Metropolis-Hastings Monte Carlo sampling.

It is anticipated that your model codes and results will provide invaluable information for prediction of this notorious invasive seaweed. 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.

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 be co-supervised by Dr. Andrew Blight and Prof. David M. Paterson in the School of Biology, University of St Andrews and will also have the opportunity to work with the physical ocean modelers in Marine Scotland.

Qualifications 

Applicants should have or expect to obtain a good honours degree (1, 2.1, or equivalent) in applied mathematics, statistics, ecology, or a highly quantitative science. Experience of numerical modelling and programming in Fortran would be highly beneficial, but not essential.

Informal enquiries can be made to the lead supervisor, Dr Bingzhang Chen, Department of Mathematics and Statistics, University of Strathclyde, Glasgow at bingzhang.chen@strath.ac.uk and/or +44(0)141 548 3286.

Formal application is via the University of Strathclyde postgraduate research application process at

https://www.strath.ac.uk/studywithus/postgraduateresearch/howtoapply/

making sure that you clearly state your interest in this project with these supervisors.

The preferred starting date is 30 September 2019.

Funding Details

Funded by NERC Studentships awarded to the SUPER Doctoral Training Partnership (SUPER DTP; https://www.masts.ac.uk/graduate-school/super/). The SUPER DTP partner Universities are St Andrews University, Aberdeen University, Edinburgh Napier University, Heriot-Watt University, the University of the Highlands and Islands, Stirling University, University of Strathclyde and the University of the West of Scotland. Underpinning these research partners, providing additional training and projects are Marine Scotland, Scottish Natural Heritage, and the James Hutton Institute, among a total of 40 stakeholder organisations including industry and government agencies and international collaborators.

Further information

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.

How to apply

Applicants should have or expect to obtain a good honours degree (1, 2.1, or equivalent) in applied mathematics, statistics, ecology, or a highly quantitative science. Experience of numerical modelling and programming in Fortran would be highly beneficial, but not essential.

Informal enquiries can be made to the lead supervisor, Dr Bingzhang Chen and/or +44(0)141 548 3286.

Formal application is via the University of Strathclyde postgraduate research application process at

https://www.strath.ac.uk/studywithus/postgraduateresearch/howtoapply/

making sure that you clearly state your interest in this project with these supervisors.

The preferred starting date is 30 September 2019.