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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 12 hours ago
and analyzing biosignals, with a preference for EEG and ECG signals. Experience analyzing EEG signals from noisy environments. Working knowledge of statistical machine learning techniques, deep learning
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, ECG, GSR and other biosignals. Multimodal data analysis Machine learning and advanced statistical modeling Research experience in perception, consciousness, cognition, or emotions will also be valued
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and uncover yet unknown physiological particularities of sleep and other human health factors. Your Profile PhD (or near completion) in Biomedical Engineering, Computer Engineering, Computer
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processing, machine learning, and human-computer interaction. The goal of this PhD is to develop neuromotor interfaces for dexterous robot teleoperation using two sensing modalities: 1) surface
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and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and electrotactile
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, engineers, and translational researchers. Research Focus Areas Applicants with expertise in one or more of the following areas are encouraged to apply: * Medical imaging AI and computer vision * Multimodal
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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biosignals. Application of machine learning techniques for classification of different classes using the extracted features. Assembly, documentation, testing, and use of an innovative biosensing system
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, visualization and reporting Apply statistical modelling, signal processing and machine learning approaches to identify phenotypes of sleep, stress, arousal and recovery Interpret complex physiological data in
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: Strong skills in developing biosignal processing algorithms and implementing machine learning models for data interpretation. Technical Oversight: Ability to monitor complex data collection processes and