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PhD student in theoretical ecology Linkoping Reference number IFM-2026-00366 We have the power of over 50,000 students and co-workers. Students who provide hope for the future. Co-workers who
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to detect by visual monitoring or because current methods struggle to separate individuals from one another. Passive acoustic monitoring, i.e. networks of autonomous recorders that capture the
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to detect by visual monitoring or because current methods struggle to separate individuals from one another. Passive acoustic monitoring, i.e. networks of autonomous recorders that capture the
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supporting resilient ecosystems in a changing world. Acoustic monitoring provides a powerful, non-invasive way to track species and their interactions over time. DC2 will use a unique 10-year acoustic dataset
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the eBird monitoring framework. This position will help define the Cornell Lab's long-term research agenda at the intersection of community science, acoustic monitoring, and statistical ecology. This
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acoustic monitoring, and statistical ecology. This position is part of a broader effort at the Cornell Lab to build the next generation of biodiversity monitoring systems. By combining the complementary
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skills, with experience in some, but not necessarily all, of the following areas: acoustic monitoring, ecological fieldwork, spatial & statistical data analysis, bat ecology, or closely related area
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Application Deadline: Applications accepted all year round Details We welcome applications from motivated PhD students interested in population ecology, demographic modelling and biodiversity science. Our
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’ distribution shifts via “data-model fusion”. The successful candidate will develop near-real-time integration between emerging environmental and ecological AI-driven data sources (e.g., automated acoustic and
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. The project focuses on filling knowledge gaps about at-risk shark, skate and ray ecology in Irish waters using acoustic and satellite tracking technology, alongside local ecological knowledge, genetic