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-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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on the following areas: Development algorithms and their software implementation in Python and PyTorch Validation of results and comparative analysis of proposed method with baseline approaches Qualifications You
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involves evaluating the economic benefit (Value of Information) of these new inventory methods compared to traditional approaches. Duties and Responsibilities: Algorithm Development: Develop and validate
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. The role involves contributing to this research project with a focus on model development, implementation, and testing. Further tasks involve dataset curation, analyzing results, and the creation
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research within Unmanned Traffic Management (UTM). In this role, your primary responsibility will be the hands-on development of advanced simulations and prototypes that help us test and validate new UTM
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degree in machine learning. The successful candidate will be supervised by professor Aristides Gionis (https://www.kth.se/profile/argioni/ ). The research team focuses on developing novel methods
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we
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situated in the field of machine learning. Potential research topics include, but are not limited to, algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine