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Field
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processing and analytical chemical analyses. •Excellent command of English language (written & oral) and experience with scientific writing. We offer •Friendly and inspiring working atmosphere in a highly
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:100% of the full-time weekly hours Tasks: The PhD student will be responsible for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs
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an emphasis on in vivo assays, animal models and state-of-the-art imaging tools Soft skills seminars on communication, presentation and other topics equally relevant for achieving success in both a scientific
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-situ measurement network and perform terrestrial laser scanning, analyzing microclimate data and their relation to forest structure, and using optical satellite time series and radiative transfer models
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journals is desired Ability to work reliably and collaboratively in a scientific research setting Strong command of spoken and written English; German language skills are a plus. We offer Involvement in a
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calculation of mixture toxicity Development and application of chemical analytical methods Application of physiology-based kinetic (PBK) modelling for the extrapolation of in vitro to in vivo concentrations
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to investigate the influence of glucosinolate-based amines on the formation of Maillard-like products in model systems and foods, to isolate and identify the resulting products and to find out whether
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parametrize wind-wave energy flux mechanisms. The overall goal of the research project is to develop process understanding and parametrizations that lead to improved, energetically consistent, climate models
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for controlled molecular transformations from small molecules to polymers. Electrical discharges in gases that operate near room temperature and at atmospheric pressure can be interfaced with aqueous solutions
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks