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such as ecology, economy and social sciences. ZMT aims to use data science tools, including computer vision and deep learning, for the study of rapid changes in tropical coastal socioecological systems
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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organised, accurate in their experimentation and adaptable to learning new techniques. Primaryresponsibilities Preparation and processing of animal histological samples, including organ embedding, cutting and
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machine learning/artificial intelligence methods in combination with complex network analysis tools to predict and model interactions between food and biological systems Further scientific development
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, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
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, candidates with expertise in machine learning could directly contribute to emission baseline prediction work whereas candidates with interest/expertise in economic theory could best contribute
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or Python Machine learning methods (for the baseline prediction for the reward funds) is beneficial We expect: Strong motivation to contribute to policy-relevant research Strong interest in teamwork and
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screening (Ulrike Haug), prevention and implementation science (Hajo Zeeb, Daniela Fuhr), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot
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intelligence (machine learning etc.) is advantageous, a focus on artificial intelligence methods in the field of material design or multi-scale simulation of non-equilibrium processes is desirable. A thematic
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further training as part of the language courses in German offered internally experience with Crop models or Forest models, and Machine learning is desirable We offer: an interdisciplinary working