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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. You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.