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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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foundational methods for integrating single-cell and clinical transcriptomes; and train, fine-tune, and validate deep learning models using multi-omics and imaging data to predict clinical outcomes such as
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
of proficiency in either R or Python in the areas of machine learning, deep learning, statistical analysis, computer vision, and/or graph analysis Experience with data engineering to create data
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adaptation; reinforcement learning and inverse reinforcement learning. o Machine Learning & Intelligence, including machine learning and adaptation; deep learning; computer vision; machine intelligence
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adaptation; reinforcement learning and inverse reinforcement learning. o Machine Learning & Intelligence, including machine learning and adaptation; deep learning; computer vision; machine intelligence
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 days ago
mathematical formalization AI / machine learning / deep learning background some Python / PyTorch experience scientific curiosity, taste and autonomy in explorative tasks and problems References [1] Toussaint
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, health professionals to stay at the forefront of medical science, and educators to advance learning. We are proud to be part of progress, working together with the communities we serve to share knowledge
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-polarized observations to retrieve ECVs such as soil moisture Bring deep learning into the retrieval chain, from raw observations to geophysical products Within this framework, you will shape your own