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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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computer science, engineering, information systems, economics, management, law, and other fields, united in pursuit of sustainable technologies that positively impact society. For more information, please visit our
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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into this material and support tailoring its properties. For this, you will: Contribute to method development for ultra-fast MLIPs (Xie et al., npj Comput. Mater., 2023) Develop realistic MD simulation protocols
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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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associated threats. The research project of the PhD student will thus focus on defining methods to track, monitor, and manage the use of GenAI. While this can rely on recentely proposed telemetry framework
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affective attitudes toward aging are assessed in a sample of N = 150 parent-child dyads – both implicitly and explicitly. Modern methods of attitude research, observation procedures, and child-friendly
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This position is inside the SPETRA doctoral training unit which investigates new materials, methods and concepts for converting sunlight into usable energy sources. Inorganic Chalcogenide
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profile described below? Are you our future colleague? Apply now! Education PhD in Computer Science with a focus on AI and/or cybersecurity Experience and skills 1-2 years of post-PhD research and
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The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg National Research Fund. MICRO-PATH, or Pathogenesis in the Age of the Microbiome (https