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to join our team at the CRSA to develop AI models, specifically deep learning approaches, to analyze and predict key climate variables such as precipitation and temperature which are essential
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) About the Project Deep learning models, and in particular large language models (LLMs), have demonstrated remarkable capabilities but remain limited by their heavy computational requirements, lack
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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
involves processing and harmonizing high-resolution NASA EO data. Subsequently, we will architect and train an ensemble of deep learning and statistical models capable of identifying key wildfire drivers and
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outstanding candidates to apply for a postdoctoral research position in Geometric Deep Learning, with a strong emphasis on applications to biology and scientific discovery. This unique research collaboration
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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SD-25157 RESEARCHER IN ATMOSPHERIC PLASMA TREATMENT OF METALLIC SURFACES FOR INDUSTRIAL APPLICATIONS
Temporary contract | 24 months | Belval Are you passionate about research? So are we! Come and join us The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology
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Job related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and
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Python and Pytorch as well as deployment on GPU clusters. A strong publication record in graph neural networks, geometric deep learning, representation learning, machine learning–based molecular/materials