The folllowing fully-funded PhD studentship is available with an expected start date of September 2022. The studentship will be held in the School of Mathematical Sciences.
Big environmental data combined with modern data science offer a key opportunity to better understand our environment and address climate change. Yet environmental data poses specific challenges due to spatio-temporal dependence: 1. Modelling. Most models of spatio-temporal dependence are homogenous, which in general is not an accurate description of real-world phenomena. 2. Estimation. More complex parametric models of covariance are often not amenable to estimation via exact likelihood due to computational inefficiency and lack of robustness to model misspecification.
In this project, you will develop new parametric covariance models and estimation methods for the analysis of Sea Surface Height (SSH). The surface height of the oceans is monitored by passing satellites on a global scale. The modelling of SSH is vital to a better understanding of the global climate and to making more accurate interpolation via kriging.
Firstly, your research will focus on estimating the parameters of a spatial covariance model of Sea Surface Height. You will pursue recent developments in quasi-likelihood estimation for spatio-temporal data to propose a parametric estimation method that is both computationally and statistically efficient. The idea behind quasi-likelihood estimation is to maximize a computationally efficient approximation to the exact likelihood.
Secondly, you will develop more advanced parametric models of covariance that can incorporate some additional physical phenomena that drive SSH. Possible directions for this part of the project range from relaxing the assumption of spatial homogeneity, to modelling temporal dependence and seasonal patterns. These methodological developments can also have an impact in other application areas such as econometrics.
This studentship is funded by QMUL. It will cover tuition fees, and provide an annual tax-free maintenance allowance for 3 years at the Research Council rate (£17,609 in 2021/22).
The project is open to UK and international students. The higher fees for international students (including EU) may be covered for up to 2 candidates applying for the Queen Mary Principal's PhD Studentships: Environment, Biodiversity and Genomics.
Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree and a masters degree in an area related to mathematics and / or statistics and / or data science. This is to be understood in a broad sense, please get in touch for an informal discussion by writing an e-mail to a.guillaumin@qmul.ac.uk
Additional skills required:
Applicants from outside of the UK are required to provide evidence of their English language ability. Please see our English language requirements page for details.
Formal applications must be submitted through our online form by the stated deadline including a CV, personal statement and qualifications.
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