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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting-edge Machine Learning applications
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knowledge of machine learning (e.g., in the areas of object detection and identification, generative AI, etc.) Good written and spoken English skills (min. level B2) Good written and spoken German skills (min
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project BEPREP (f/m/d) Your tasks: Apply and further develop machine learning methods for the analysis of health and climate data Conduct spatio-temporal analyses of patient and climate datasets to identify
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Lehrstuhl für Nachhaltige Thermoprozesstechnik und Institut für Industrieofenbau und Wärmetechnik | Aachen, Nordrhein Westfalen | Germany | about 1 month ago
. Methodological knowledge in the field of machine learning is an advantage. You have a high level of independence and commitment. You would like to develop and realise your own ideas. You enjoy working in a team
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research (http://www.wzb.eu/en ). WZB researchers come from across the globe and work across social science disciplines, including sociology, political science, economics, law, and psychology. A member of
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of the hazardous space radiation environment and developing tools for the prediction of the adverse effects of the space environment, utilising satellite observations, physics-based numerical models, machine
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hosts. The project is centered on the integration and analysis of multiomics datasets utilizing advanced machine learning approaches and biological network analysis. The successful candidate will join an
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funded German research initiative. Project Description: Carbon black is an indispensable component of numerous everyday products – from car tires and seals to paints and plastics. However, its production
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM