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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 4 hours ago
Candidates should have a PhD in physics or a related field, and will be evaluated on their academic credentials and their prior research record. Preferred Qualifications Experience with machine learning (ML
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Experience in the broad area of computational modelling of chemical and biological systems Experience with molecular dynamics (atomistic or coarse-grained) Experience with machine learning potentials or force
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, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description We are seeking
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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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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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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 hydraulic flood
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 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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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments