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Field
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approaches that combine deep learning with classical reasoning. Location Hybrid/Flexible Work Arrangement Additional Information This is a Part-Time Temporary position classified as Non-Exempt. The offered pay
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or edge AI deployment. Strong programming skills in C, C++, and Python, with experience in deep learning frameworks such as PyTorch or TensorFlow. Familiarity with deployment constraints on cloud, edge
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of the project. Job Requirements: A PhD degree in mathematics, AI or related areas, with a strong background geometric/topological/algebraic data analysis, geometric/topological deep learning, molecular sciences
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advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities such as CT, MRI, X-ray, and ultrasound. Research areas include image segmentation, detection
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communication skills. Proficiency in developing deep learning models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in
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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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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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of Tokyo and Associate Professor Atsushi Nitanda from A*STAR. Key Responsibilities: Research & Development Conduct original research in deep learning, related to uncertainty quantification for large models