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., atmospheric correction), spectral unmixing, and variable retrieval, feature extraction. Experience working with cloud-based platforms and large-scale EO data workflows. Strong programming skills with experience
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) and reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing
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– ‘“Seeing through clouds” satellite remote sensing by unifying optical and SAR sensors’. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no more than five
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security mechanisms connected to the cloud in residential environments and intrusion detection methods in the context of embedded systems and IoT devices. The work will explore machine learning techniques
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of radiative transfer models to study various essential climate variables (clouds, aerosols, surface properties, greenhouse gases); (iii) validation activities. The postdoctoral researcher will work under the
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for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross
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experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer-reviewed publication Strong communication and
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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PyTorch or ONNX. Demonstrated experience working with large-scale data processing and deployment (local and cloud) including SQL database technologies. Demonstrated ability to represent complex data