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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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Job Description Do you want to figure out why Bayesian deep learning doesn’t work? And afterwards fix it? At DTU Compute we are working towards building highly scalable Bayesian approximations
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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independently, collaborate across disciplines, and communicate scientific results effectively in English Desirable competences Knowledge of CT reconstruction or spectral imaging physics Experience with deep
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serving (Ray/VLLM), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression Extensive knowledge of agentic AI systems research, engineering and
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of operating underground with minimal human intervention. By joining this project, you will strengthen your scientific profile while gaining deep hands-on experience in mobile manipulation, contact-rich robotics
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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environmental changes, agricultural management, and ecosystem sustainability Experience with deep learning, radiative transfer modeling and ecosystem modeling Teaching and supervision experience Who we
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environmental changes, agricultural management, and ecosystem sustainability Experience with deep learning, radiative transfer modeling and ecosystem modeling Teaching and supervision experience Who we