Vacancy for post-doctoral Ecological Statistician at Centre for Research 
into Ecological and Environmental Modelling (CREEM), University of St 
Andrews

Vacancy Description

School of Mathematics and Statistics, Salary: £33,199 - £36,261 per 
annum, Start Date: 1 September 2019 or as soon as possible thereafter, 
Fixed Term for 18 months

This is an 18-month fixed-term post. We are looking for an ecological 
statistician to work as part of an inter-disciplinary 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 algorithms for fitting 
multi-species dose-response functions and applying them to experimental 
data to determine which species can be grouped together in terms of 
their responsiveness to sonar; (2) comparing methods for inferring 
behavioural response from long-term passive acoustic data collected 
during observational studies in the vicinity of Navy sonar exercises.

The first task builds upon established Bayesian methods for modelling 
probability of behavioural response as a function of sonar dose (Miller 
et al. 2014, Antunes et al. 2015).  These focused on single-species 
analyses. Extensions have been made that allow for multi-species 
modelling, including model selection for which species should share 
common dose-response functions and which should be different.  However, 
the current implementation is computationally expensive and hence a 
bespoke algorithm, likely based on reversible jump Markov chain Monte 
Carlo (RJMCMC), is desired.  Analyses of real-world experimental data 
already collected is also part of this task.  The second task will 
involve, at least partly, a simulation study comparing two approaches 
that have already been applied to long-term passive acoustic data (e.g., 
Oswald et al. 2016): Generalized Estimating Equations (GEEs) and Hidden 
Markov Models (HMMs).

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 Bayesian analysis, RJMCMC, HMMs and 
GEEs would be highly advantageous, but is not a requirement.  The 
overall project is a collaboration between ecologists, statisticians, 
oceanographers and acousticians and so the ability to communicate 
effectively between disciplines is essential.

The successful candidate will be based in the world-leading Centre for 
Ecological and Environmental Modelling (CREEM) under the supervision of 
Professor Len Thomas and Dr. Catriona Harris.  CREEM has an excellent 
record of retaining research staff, so while this is a fixed-term 
18-month post there may be prospects for continuation beyond that period.

For informal enquiries, we encourage those considering applying to 
contact Professor Len Thomas ([log in to unmask]) and/or Dr. 
Catriona Harris ([log in to unmask]).

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/.

Post: Research Fellow - AR2228HM

Closing Date: 5 July 2019

Please quote ref: AR2228HM

Summary info: https://tinyurl.com/y3najjvx
Further Particulars: https://tinyurl.com/yxo2yvb4

-- 
Len Thomas  [log in to unmask]  lenthomas.org  @len_thom
Centre for Research into Ecological and Environmental Modelling
The Observatory, University of St Andrews, Scotland KY16 9LZ
Office: UK+1334-461801  Admin: UK+1334-461842

The University of St Andrews is a charity
registered in Scotland, No SC013532.

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