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Identification of soil invertebrates (e.g. mites, springtails, insects) using modern and classical techniques Laboratory analyses of soil properties Statistical analysis of complex ecological datasets Presentation
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 15 days ago
develop state-of-the-art theoretical and computational methods to model strong light–matter coupling in and out of equilibrium, across gaseous, liquid, and solid-state systems. Key responsibilities The PhD
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
statistics and the ability to apply quantitative analysis to ecological data A strong background in programming (preferably in R), including data manipulation, statistical analysis, and spatial modelling and
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(e.g., GIS, system dynamics, statistical programming) #strong knowledge of qualitative methods in empirical social research #experience working in interdisciplinary and international research projects is
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of climate model output by means of classical statistical and machine-learning methods #coordination of scientific workflows among project partners Your profile #Master's degree and PhD degree in meteorology
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
ecosystems. Your Profile A PhD in marine biology, conservation biology, fishery management & conservation, or related fields A strong background in handling large data sets, programming (preferably in R
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, signal processing, and data mining A strong background in programming, statistical analysis, and spatial modelling and mapping Highly motivated to work on the subject and eager to work in an
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degree and PhD degree in meteorology, oceanography, physics or mathematics, with a strong interest in the application of statistical and data analysis methods excellent knowledge in UNIX/Linux and Unix
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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, probabilistic models Representation learning, self-supervised learning, foundation models Data analysis, non-linear statistics, knowledge management Your profile PhD in Computer Science, Bioinformatics