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to mechanistically explore the interaction of phytonutrients in the plant-environment response in order to enhance and secure the nutritional quality of vegetables under changing climatic conditions and under
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-surface monolayers. Understanding energy and scalar fluxes across the air-sea interface is crucial for a number of environmental challenges, including weather, climate and sea state forecasting. The Air-Sea
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positions (TV-L E13). Addressing global challenges, the school provides a wide variety of topics, from logic in autonomous cyber-physical systems to machine learning in Earth System models. You will have one
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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the distribution of water vapor by means of machine learning approaches and to improve atmospheric correction beyond standard approaches. The research work is expected to contribute in two ways: (i) the separation
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areas: software testing, runtime monitoring or diagnostics combined with a desire to embed these in today's assurance processes. Knowledge of software development methods (especially agile methods such as
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modern models beyond the worst case e.g. integrating machine learning into algorithm design. We are looking for candidates with a strong mathematical background, an excellent degree in mathematics
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for rapid and label-free monitoring of biomolecules, such as disease biomarkers, drugs, and biogenic gases. We work at the interface between physics, chemistry, engineering and life sciences. We research
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of vegetables under changing climatic conditions and under the impact of the food supply chain, including food processing. In this context, the aim of the PhD project is to investigate the influence of drought
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self-learning IT systems. This requires disruptive concepts involving new non-volatile storage devices from new materials combined with innovative circuit designs to overcome the performance limits