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and robot-based paradigms. We are also leveraging several analytical tools such as computational modelling and machine learning. As a Research Assistant, you will help collect, analyse and interpret
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scholarly community. The Department is seeking candidates involved in computational/theoretical approaches that bridge the gap between neuroscience and artificial intelligence, machine learning, and other
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reconstruction), machine learning (including big data analytics and adversarial machine learning), natural language processing (audio-visual multimodal understanding), autonomous systems (such as driverless cars
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single coherent framework that truly understands its environment. You have a master's degree in Computer Science, Artificial Intelligence or similar. You are interested in Logic, Machine Learning
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statistical, computational, and/or machine learning methodologies to address them. The search is broad and welcomes research spanning modern statistical methods, machine learning, AI, and emerging approaches
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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, hardware development, machine learning and signal processing, and system integration. We are interested in working on different areas to improve the BCI technology. These areas include (but are not limited
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: Machine/deep learning, artificial intelligence, statistical modeling, or computational modeling Human neuroimaging analysis, including functional MRI, structural MRI, connectivity, or brain circuit
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Professor in Psychology. We welcome outstanding candidates from all areas of psychology whose research applies computational approaches, including machine learning and artificial intelligence, to address
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University of California, Los Angeles | Los Angeles, California | United States | about 20 hours ago
. Additional Qualifications: Background in bioinformatics systems, connectomics network analysis and machine learning modeling confer a very significant advantage. The shared values of the DGSOM are expressed