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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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systems at various scales, for example using ab initio electronic structure methods like density-functional theory, developing interatomic potentials with various methodologies including machine learning
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 6 days ago
shape the next generation of agentic AI tools for biomedical research. A highly interdisciplinary environment connecting AI, computational biology, human–computer interaction, and research software
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on advanced machine learning and emulation approaches. Key responsibilities: The candidates will be expected to work on the following tasks: - Develop machine learning (ML) methodologies appropriate
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Join and help us to derive global forest biomass data from the European Space Agency’s Biomass satellite mission. If you have interests in remote sensing, machine learning and forests, this is the
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, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact
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of the postdoctoral scholars. https://postdoc.ucsd.edu/postdocs/px-contract.html Qualifications Basic qualifications (required at time of application) Doctorate degree or equivalent terminal degree in Engineering
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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develop methods to disentangle dynamic, multiscale ecological signals from large, heterogenous observational data. This work lies at the interface of statistics, machine learning/AI, ecology, and