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Field
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designing to new neuromorphic circuits with enhanced modulation properties, for real-time processing and learning of sensory processing tasks such as biomedical signal processing and adaptive sensorimotor
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with deep learning and 3D point clouds Apply for this job See advertisement About the position The Faculty of Environmental Sciences and Natural Resources Management (MINA) at Norwegian University
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modeling in calculation processes and application of AI techniques for processing dense point clouds Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research
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experience. Your CV should demonstrate the scope of your work and education and indicate a good fit with the stated requirements for the position. A motivation letter (max 1 page) emphasising your specific
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the electrical contacts and magnetic field to influence the fault current interruption process. The PDRA will contribute mainly to the arc modelling part of the project and play an active role in organising and
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, signal processing and/or wireless communication. Basic knowledge of and/or experience in working with reinforcement learning/other machine learning algorithms Excellent command of spoken and written
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classification of EEG and auditory signals. The group of the project is multidisciplinary, with experts in signal processing, machine learning, acoustics and language. The successful applicant will perform
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: Grade point average of the academic transcripts from the applicant's bachelor's or master's degree studies (or 1st and 2nd cycle): up to 5 points out of a total of 10. Curricular merits: up to 1 point out
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Oldenburg Oldenburg, Niedersachsen | Germany | 23 days ago
description:PhD position in cellular signaling in photosymbiosis (f/d/m) Background We invite applications for two fully funded PhD positions (3 years each) to join the newly established photosymbiosis focus group
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integrate multiple signals during communication (e.g., using insights from psycholinguistics and neuroscience to inform computational models of multimodal language processing- Building AI agents that engage