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energetic analyses ● molecular/eDNA diet analysis ● stable isotope analysis ● contaminant sampling ● acoustic benthic mapping ● ecological modeling The postdoc will work closely with collaborators at UC Santa
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machine learning. You enjoy applying these modelling skills to analyse and explain interactions within environmental and ecological systems Able to handle and integrate multi-temporal and multi-spatial data
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must have: • A PhD in evolutionary ecology, ecological physiology, microbiology, bioinformatics, ornithology, or another field relevant to the position. • Strong bioinformatic skills and experience with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 25 days ago
with related fields, including marine biogeochemistry, physical oceanography, environmental genomics, remote sensing, ecological modeling, and/or data science (AI and machine learning). We
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» Ecology Environmental science » Water science Environmental science » Other Researcher Profile Recognised Researcher (R2) Application Deadline 7 Jan 2027 - 22:59 (UTC) Country Switzerland Type of Contract
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, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using large-scale
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will be embedded in the Soil Ecology Group of Andreas Richter (https://ter.univie.ac.at/richter ) at CeMESS, an internationally renowned centre in microbial ecology, soil ecology, and ecosystem science
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directly measuring behaviour and/or reductions in pollutants or indirectly through modelling or proxy metrics). The ECOSOLUTIONS DFA Led by the Universities of Sheffield and York and the UK Centre
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for Applied Marine Ecology and Modelling (for more information see: https://ecos.au.dk/en/researchconsultancy/research-areas/applied-marine-ecology-and-modelling). The department is, and wishes to continue to
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negotiable. Key areas of responsibility include: ● Leading the development of qualitative and quantitative systems models that synthesize ecological, social, economic and policy drivers of grassland conversion