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, particularly of non-model organisms Computer programming and experience with the solution of numerical problems, machine vision, and analysis of next-generation sequencing data High-throughput screening
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science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description The successful
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atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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research and experimental data in accordance with established protocols. Assist in statistical and comparative analysis of experimental data using appropriate computer software. Contribute to the preparation
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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to these values. We are seeking an excellent candidate for a Postdoctoral Research Associate in 3D Deep Learning for Vision and Lidar position who has: a PhD (or near completion) in a relevant field background
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work in applications
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive