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Field
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master’s degree in a relevant field (e.g., cognitive neuroscience, biomedical engineering, biological psychology) experience with programming (e.g., MATLAB, Python) hands-on experience with neuroscientific
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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psychology) experience with programming (e.g., MATLAB, Python) hands-on experience with neuroscientific research good organizational and communication skills to professionally interact with patients and
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of energy-aware planning and scheduling in manufacturing, -experience in programming (Python, C/C++, Java, etc.) and implementing intelligent automation, -previous participation in EU or international
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models. Experience (or strong willingness to learn) in programming and data analysis (e.g. Python, MATLAB, R, Fortran, or similar). Curiosity and motivation to work on fire emissions, air quality, and
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between passport templates and openLCA, enabling rapid recalculation of environmental impacts under different reuse or recycling scenarios. This approach prioritizes the methodical adaptation of existing
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 1 month ago
be established for the combination of various optical sensor data sets covering different vertical, horizontal and temporal scales. Specifically the information on particulate and dissolved organic
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at different levels to upscale the processes and quantify larger scale greenhouse gas emissions. There is an opportunity to work together with a larger group of researchers within ongoing projects
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programming (Python, C/C++, Java, etc.) and implementing intelligent automation, -previous participation in EU or international projects. The candidate is expected to demonstrate strong commitment and self
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of upscaling of permafrost thaw and greenhouse gas emissions. The candidate will work with ample of existing observational data at different levels to upscale the processes and quantify larger scale greenhouse