The PhD student will be supervised by an interdisciplinary team across four international institutions, with expertise in
- Statistics (Dr Bruno Santos) and Statistical Ecology (Dr Eleni Matechou), University of Kent, Canterbury, England
- Demography (Dr Eleonora Mussino), University of Stockholm, Sweden
- Bayesian Inference/Statistical Ecology (Professor Ruth King), University of Edinburgh, Scotland
- Ecology/Statistical Ecology (Dr Blanca Sarzo), University of Valencia, Spain
Monitoring the size and characteristics of human populations using official censuses is a lengthy and costly process. As a result, in recent years, there has been an increased focus on using a statistical framework instead, referred to as multiple systems estimation (MSE), to infer population size of specific groups using opportunistic registers and incomplete lists. Examples include estimating the number of drug users in a region  and the number of victims of human trafficking .
MSE builds on well-established statistical theory and models, and can be employed using existing software, such as R. However, MSE does not follow the same individuals over time to learn from their past experiences or behaviours and therefore cannot account for individual heterogeneity in the probability of being observed in one or more registers, for dependence between individuals or to identify the factors behind temporary emigration.
These incomplete and imperfect human population registers are similar to ecological data, referred to as capture-recapture (CR) data, collected on wildlife populations, such as birds and mammals . The corresponding ecological CR models have the same aims as MSE, namely estimating population size and monitoring population characteristics, but rely on completely different modelling approaches, with a strong focus on modelling individual time series . However, CR models are computationally more demanding than MSE, and as a result do not scale well to large populations .
The project aims to bring together MSE and CR modelling approaches and provide a general and unifying modelling framework for human population registers. The new models to be developed will overcome the shortcomings of the existing approaches, and so will be applicable to high-dimensional data sets typically observed in human populations, and increasingly in wildlife populations, whilst at the same time modelling individual time-series data.
The student, under the guidance of the supervisory team, will develop sophisticated models for high-dimensional, long time-series data, numbering millions of individual cases. They will also implement, extend and develop corresponding Bayesian algorithms for fitting the models to real data from several countries such Sweden, Norway and Italy. They will also extend and adapt the models for corresponding large ecological data.
We seek a candidate with a strong quantitative background, for example an MSc in Statistics or an MSc with high statistics content, or a background in demographic modelling. Experience coding in R, or similar, is essential.
The student will join the thriving Statistical Ecology @ Kent research group, and the Migration and Movement Signature Research Theme, and will be supervised by leading researchers in demography, statistics and statistical ecology. They will also be members of the UK-wide National Centre for Statistical Ecology. They will attend London Taught Course Centre training, NCSE seminars, and SE@K specialist training and they will present research results at a range of appropriate national and international conferences. There will be ample opportunity for independent development, with the student gaining transferable knowledge of modern data science and statistics that will be particularly applicable for careers in demography, conservation and other applied fields.
Please follow the link below for more information on the project, the supervisory team, and on how to apply https://blogs.kent.ac.uk/seak/2022/11/22/migration-and-movement-srt-project/ and email Dr Eleni Matechou ([Email Address Removed]) if you are interested in applying for the project or have any questions about the project or the application process.