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    Quantitative Ecologists

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    The Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) in Berlin is offering a PostDoc position for Quantitative Ecologists. The position is part of an interdisciplinary project funded by the Federal Ministry of Education and Research (BMBF) on 'SmartPatrol: AI against poaching – dynamic real-time optimization for efficient protected area patrols'. The position will be embedded in the Department of Ecological Dynamics at the Leibniz-IZW and will focus on the development and application of modern approaches to process and analyze wildlife survey data. The position offers a stimulating international research environment in an interdisciplinary, collaborative project employing state-of-the-art methodology.


    • Development and application of modern approaches to process and analyze wildlife survey data
    • Analyzing camera-trapping and SMART patrol data using a (community) occupancy framework
    • Co-development of an R Shiny App to make analytical approaches available to a broader scientific and conservation community
    • Co-development of AI (convolutional neural network) approaches to locate and classify animals in camera-trap images
    • Involvement in developing a proposal for continuation of the project


    • PhD in ecological statistics, ecology or related fields with a strong analytical/quantitative/methodological focus
    • Experience with statistical hierarchical modeling frameworks for wildlife survey data, ideally community occupancy modeling
    • Excellent programming and scripting skills in R, BUGS/JAGS (and/or Python)
    • Broad experiences working with wildlife community datasets
    • Strong documented ability to publish in peer-reviewed journals
    • Organizational skills, high motivation, and capable of working both independently and as part of a team
    • Proficiency in English (both spoken and written)
    • Experience in spatial data analysis (QGIS, R spatial “ecosystem”)
    • Experience in developing R Shiny apps
    • Experience in R package development
    • Experience in working with convolutional neural networks for image classification and object detection (e.g. in Keras, TensorFlow)

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