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: Completed master studies in the field of environmental sciences, forestry, landscape ecology, remote sensing or related fields Interested in remote sensing, quantitative methods and programming Prior
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epidemiology. This collaborative environment fosters innovation and skill development, providing hands-on training in organoid culture, pollutant exposure methods, and data analysis. Additionally, through a
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& Technology and explore how emerging technologies (AR, VR, AI) shape decision-making. YOUR PROFILE - Excellent Master’s degree in human-computer interaction, game engineering, psychology, or computer
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knowledge of quantitative methods, particularly in statistics and econometrics; experience in machine learning is a plus Background in business/management/behavioral science Experience with programming
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(https://soilsystems.net/ ), a Priority Programme (SPP 2322) funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation). Within SoilSystems, scientists from different disciplines from
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, Human-Computer Interaction, and their responsible applications. Ideal candidates will have: An M.Sc. degree (or equivalent) in Computer Science, Game Engineering, Mathematics, Statistics, or related
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these changes, identify their causes and describe their impacts on biodiversity and ecosystem services. To do this we use a combination of diverse methods, from empirical research to remote sensing and simulation
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these materials into high-performance fibers and functional materials. By manipulating molecular interactions through chemical and physical methods, we tailor the structural and mechanical properties of bio-based
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parameters to understand hydraulic thermal processes, develop innovative monitoring systems, evalua-tion and implementation methods like geothermal potential assessment as well as numerical reservoir modelling
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methods (such as Machine Learning, Metric Learning, Reinforcement Learning, Graph Representation Learning, Generative Models, Domain Adaptation, etc.) for Design Automation applications. To this end, we