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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 27 days ago
differential equations # Knowledge of Scientific computing / high-performance computing # Knowledge of Algorithmic modeling and simulation # Fundamentals of machine learning or quantum computing # Programming
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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institution and is The Military College of Georgia. More details on the UNG Mission, Values, Vision, and Culture can be found at https://ung.edu/about/mission-vision-values.php Offers of employment
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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comprehensive and competitive medical and dental care programs, generous retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture
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datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in