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for showcasing the improved mapping and monitoring of forest traits and uncertainties. You will be mainly in charge of: Develop improved hybrid model inversion methods with a focus on machine learning and deep
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LIST? Check our website: https://www.list.lu/ How will you contribute? The Post-Doc researcher will develop, implement, and apply advanced ways in inverting a radiative transfer model for forest trait
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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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to machine learning and AI projects for satellite systems. We are looking for a candidate capable of developing ML models and optimization algorithms specifically designed for highly dynamic satellite
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years of postdoctoral experience
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harmonization, multi-omics integration as well as the development of machine-learning models for patient stratification and outcome prediction. Moreover, complex multi-layered datasets shall be integrated
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to test and compare strategies safely, calibrate models with real data, and support scenario-based decision-making. • Building data-driven models (e.g., forecasting, clustering/segmentation, learning-based
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their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? You will be part of LIST’s Remote sensing and natural resources modelling group Embedded in