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Field
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this short-term project we shall use active learning to accelerate the training of deep learning algorithms for optimising 2D material van der Waals (vdW) structure discovery. The goal is to make model
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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in clinical trials. Regularly report on improvements or challenges. Documentation will occur via an online database 4. Support training and testing sessions: Coordinate the scheduling of training and
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: Coordinate the scheduling of training and testing sessions and prepare the facilities and equipment for training sessions and data collection. 5. Ensure safety and compliance: Ensure the safety and well-being
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
relays, the burden on DSN scheduling can be significantly reduced. The use of frequency division (OMSPA) enables all probes communicating through the relay to be received by one DSN antenna, enabling DSN
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, the assistant will preprocess raw omics data, conduct exploratory and multivariate analyses (e.g., PCA, CCA), and iterate on new algorithms for mutant detection and pathway discovery. The role is ideal for a
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Group , a leader in innovative multi-sensor atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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Reconstruction from clinical arthroscopy video. The research will focus on investigating and advancing multi-view feed-forward algorithms to tackle the specific challenges of the arthroscopic environment, where