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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
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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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research university located in the heart of Detroit, Michigan where students from all backgrounds are offered a rich, high-quality education. Our deep-rooted commitment to excellence, collaboration
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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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Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep learning, with experience in time series forecasting or related
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strong asset. Qualifications: A PhD or equivalent qualification in a relevant field, such as marketing, consumer behavior, psychology, behavioral economics, sustainability science, or related social
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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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partners, or translational research teams. Experience with Quarto, Python, and/or other programming languages. Experience and interest in data science or informatics education. Experience with deep learning
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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