This is a 2-year fixed-term post. We are looking for an ecological statistician to collaborate with statisticians and ecologists working on a project to develop new quantitative models and analytical methods for inferring behavioural response of marine mammal species to US Navy sonar (see Harris et al. 2018 for a recent review). The post will involve working on two tasks within the larger project: (1) developing continuous-time movement models that can be used to infer underlying behavioural states by fusing information from multiple data sources (different types of animal-borne tag, observational data, etc.) collected at a range of spatial and temporal scales during Controlled Exposure Experiments on free-ranging cetaceans; (2) comparing methods for inferring behavioural response from long-term passive acoustic data collected during observational studies in the vicinity of Navy sonar exercises.
Both tasks involve modelling a stochastic process in time. The first task will focus on developing efficient, practical methods to fit continuous-time behaviour-switching animal movement models (Blackwell 2018) using their representation as stochastic differential equations (Pedersen et al. 2011) and their approximation by finite element analysis (Ovaskainen 2008). The first task will also develop a state-space framework to describe how animal behaviour changes with respect to navy sonar and how long these effects last. The second task will involve, at least partly, a simulation study comparing two approaches that have already been applied to this type of data: Generalized Estimating Equations and Hidden Markov Models (e.g., Oswald et al. 2016).
We welcome applications from candidates with a PhD in Statistics or a closely-related discipline, who have an interest in using and developing statistical methods to solve real-world problems in ecology. Experience with and/or research interests in movement modelling methods (including hidden Markov or state space methods, possibly applied in non-movement modelling contexts), partial differential equations (and finite element analysis), or experience with generalized estimating equations would be advantageous, but is not a requirement. Experience developing methods for modelling data in both Bayesian and classical frameworks is also advantageous. The overall project is a collaboration between ecologists, statisticians, oceanographers and acousticians and so the ability to communicate effectively between disciplines is essential. The project will involve several face-to-face meetings with other project partners in the USA.
The successful candidate will be based in the world-leading Centre for Ecological and Environmental Modelling (CREEM) and will work with statisticians at CREEM, acousticians and marine mammal biologists in US academic institutions, the US Navy and US non-governmental organizations. Work on task 1 will be supervised by Dr Richard Glennie and work on task 2 will be supervised by Prof. Len Thomas.
CREEM has an excellent record of retaining research staff, so while this is a fixed-term 2-year post there may be prospects for continuation beyond that period.
For informal enquiries, we encourage those considering applying to contact Professor Len Thomas (len.thomas@st-andrews.ac.uk) and/or Dr. Richard Glennie (rg374@st-andrews.ac.uk).
Applications are particularly welcome from women, who are under-represented in Science posts at the University. You can find out more about Equality and Diversity at http://www.st-andrews.ac.uk/hr/edi/.
The University is committed to equality for all, demonstrated through our working on diversity awards (ECU Athena SWAN/Race Charters; Carer Positive; LGBT Charter; and Stonewall). More details can be found at http://www.st-andrews.ac.uk/hr/edi/diversityawards/.
Please quote ref: AR2147SB
Closing Date: 26 October 2018
Further Particulars: AR2147SB FPs.doc
School of Mathematics & Statistics
Salary: £33,199 - £39,609 per annum
Start: 3 January 2019 or as soon as possible thereafter
Fixed term for 2 years