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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 5 hours ago
publications or manuscripts in preparation. · Demonstrated experience applying deep learning techniques in geospatial or spatial data analysis (required). · Experience with generative AI models (e.g., diffusion
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using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Successful
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of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in psychiatric disorders
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statistical and deep learning-based methods; * Construct AI-driven models for mosquito habitat mapping, integrating diverse climate change scenarios to enhance predictive accuracy and future forecasting
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 10 hours ago
designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology. Description: Exploration to deep space will require unprecedented
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning for biomedical data will be advantageous. The successful candidate will contribute to high-impact publications
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generation into dairy farming, by combining it with recent technological developments in the energy sector. This project will investigate a range of topics relating to AI (deep learning, reinforcement learning
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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learning, particularly in deep learning or related areas. No prior knowledge of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications