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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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. • Contribute to interdisciplinary research projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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modulation, radar/sonar signal processing, and machine and deep learning. WORK PERFORMING: Individual will perform research generally in the area of statistical signal and array processing. Topics of specific
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Earth Observation data analysis and/or spatial modeling Proven ability to publish in high impact peer-reviewed international journals Experience with machine/deep learning / AI applied to environmental
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electrocatalytic processes. This postdoctoral position will investigate pulse-mediated electrodeposition of metal particles using deep eutectic and organic solvents, ultimately aiming to induce kinked high-index
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communication signals on our mental processes. What you will do Build novel deep learning architectures for auditory prediction (speech and/or music) prioritizing explainability and cognitive hierarchies Quantify
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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum