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and science communication contribute to PRIF's success. We are looking for a new colleague to join our CNTR Research Group “Chemical and Biological Weapons Control” that is part of our Research
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Excellent command of advanced statistical methods, such as network analyses, using the R programming language Strong publication record relative to the career stage, demonstrating research excellence and
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of observations have allowed us to make advances in unravelling some of these interactions, such as the tectonic controls on diversity at global and regional scales. However, not many models exist that couple eco
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to design and analyse on-farm experiments motivation to collaborate with international and regional project partners e.g. HNE Eberswalde excellent command of English (German is an additional asset but not a
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of cardiovascular imaging is desirable but not required. What we offer: WIAS Berlin is a premier research institution known for its strength in optimization, optimal control, dynamical systems, and applied
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multidisciplinary team from several project partners (incl. IPK Gatersleben and Justus-Liebig-Universität Gießen) to analyze how controlled abiotic stress regimes (drought and flooding under different seasonal
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boundary at the mesopause allows conducting controlled experiments to delineate the effects of lower-atmospheric forcing. To achieve this, the successful candidate will set up TIE-GCM to be driven with
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. Implement Advanced Characterization Techniques to evaluate material performance and device reliability, integrating electronic control systems including DC-DC voltage boosting and maximum power point tracking
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to design and analyse on-farm experiments motivation to collaborate with international and regional project partners e.g. HNE Eberswalde excellent command of English (German is an additional asset but not a
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analyzing large-scale datasets using software tools like Python Proven track record of publishing high-quality articles in peer-reviewed journals Excellent command of written and spoken English Familiarity