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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | 29 minutes ago
. Description: Exploration to deep space will require unprecedented levels of reliability and resilience to ensure mission success. Studies have shown that a high percentage of faults that occur onboard
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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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out rigorous and impactful research into the computational mechanisms of human learning using deep neural network models, and disseminating the findings within the research group, across the wider
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work with deep learning frameworks (e.g., pyTorch, TensorFlow) Proven publication record in relevant fields Familiarity with high-performance computing environments Familiarity with using and adapting
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learning conferences and journals. Be a part of a collaborative research environment which will provide the opportunity to perform cutting-edge research in deep learning and scientific computing. Deliver
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Neural Networks Deep Learning and Uncertainty Quantification Python and ML frameworks (TensorFlow, PyTorch, JAX) Reproducible and open-science practices Experience with geospatial, environmental
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of Information Technology, and will be conducted alongside other researchers at the Centre for image Analysis who develop computational methods with a particularfocus on deep learning and image analysis. The project relies
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
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developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients or internal users. The data scientists in INGA furthermore
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related field are particularly encouraged to apply.We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular