Statistical Ecologist Vacancy at CREEM

2-year, fixed-term post for a statistical ecologist.

This is a 2-year fixed-term post. We are looking for a statistical ecologist to collaborate with statisticians and ecologists working primarily on two distinct but related projects. The first project involves modelling the movement of whales tracked by localizing their vocalizations on an array of acoustic sensors, and inferring whether their vocal and/or movement behaviour changes in relation to presence of naval sonar.  The second involves providing statistical support and analysis for an initiative to conduct the first global survey of snow leopard populations, in collaboration with the Snow Leopard Trust (SLT: and the Global Snow Leopard Ecosystem Protection Programme (GSLEP:  

Although the two problems are different ecologically, there is some overlap in the statistical methodology. This is primarily in the area of modelling animal movement from locations of individuals obtained from either animal-borne tags or acoustic localizations. Movement modelling is the focus of the whale project but is also a component the snow leopard project, where analysis of data from tagged animals will be important for understanding leopard movement.  Key statistical methods that may be used are hidden Markov and state-space modelling.  The snow leopard project primarily involves estimation of population size from spatial capture-recapture surveys with camera traps, and occupancy data, and associated survey design questions.

In addition to these two projects, the post may involve some time (up to 4 months) working on other research projects in the related fields of movement modelling, wildlife population assessment or estimation of behavioural response to disturbance.

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 wildlife survey methods, spatial capture-recapture methods, distance sampling methods and/or movement modelling methods (including hidden Markov and/or state space methods, possibly applied in non-movement modelling contexts) would be advantageous.  Experience of modelling data in both Bayesian and classical frameworks is also advantageous.

further details

contact: Prof Len Thomas

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publish: 29-05-2018 to 20-06-2018