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Field
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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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Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction
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implement state-of-the-art data science principles into dental practice. While our primary focus is on the use of deep learning in (dental) imaging, our work expands into any type of data (e.g. tabular data
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in and motivation for genomics or/and biodiversity conservation studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL) methods. Demonstrated proficiency in
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biologically-inspired deep learning and AI models (NeuroAI). The computational models we work with include vision deep learning models (including topographical, recurrent, or developmentally inspired models
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foundation in machine learning, deep learning, or computer vision Proficiency in Python and experience with ML frameworks such as PyTorch or TensorFlow Demonstrated research productivity (e.g., peer-reviewed
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cohorts with rich clinical data. Works on advanced modeling of post tuberculosis lung disease risk using approaches including generalized organoid models, and linear models and deep learning. Minimum
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 3 hours ago
mathematical and computational models of the coupled thermo-fluid dynamics of wind and surface-wave driven shelf-water polynyas in polar oceans. As magnets for biological activity, centers for deep salty water
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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techniques and genomic data analysis. Background in and motivation for genomics or/and biodiversity conservation studies. Strong background in AI/ML fundamentals and extensive experience with deep learning (DL