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Field
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will design and implement novel computer vision and machine learning methods for “sensorized” cameras that extract medically relevant features without transmitting raw video. You will evaluate algorithms
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. This postdoc position will utilize
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the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. The postdoc will be part of the Microbial Metagenomics group
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Mississippi. Apply computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate
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-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a travel allowance and access
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours 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
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 18 days ago
systems Computer science » Informatics Computer science » Programming Researcher Profile Recognised Researcher (R2) Country Czech Republic Application Deadline 29 Nov 2025 - 23:00 (UTC) Type of Contract
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proficient in working with large data-sets (observational or numerical), machine learning, and running/analyzing numerical simulations. The initial appointment will be for two years at Rutgers University
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statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results at consortium meetings
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theory, and machine learning applications in physics. The initial appointment is for one year, renewable for up to two additional years, depending on research performance and funding. Applicants should