Quantitative Ecologists
Einführung
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.
Aufgaben
- 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
Vorraussetzungen
- 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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