1,058 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"Ulster-University" Fellowship positions
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Active participation in LALP Lab activities Required selection criteria You must have completed a doctoral degree in cognitive science or computer design/programming Training and experience with at least
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validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning physics, neuroscience and
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow. We welcome you to join our community
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Job Description The Centre for Machine Learning within the Data Science and Statistics Section of the Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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electrophysiological recordings in humans with behavioral experiments and advanced analytical approaches, including machine learning and statistical modeling. It has two main objectives: Develop a cognitive task for
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career Use and/or development of advanced stellar photometric/spectroscopic/spectropolarimetric methodologies Experience with machine learning techniques Experience with pipeline development and testing
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Earth Engine, ENVI, MATLAB, or R. Desirable Proficiency in applying machine learning methods to multispectral and hyperspectral data for detecting crop diseases and estimating crop yield and quality
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deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in human factors with applications
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machine learning with rigorous statistical methods to address fundamental questions in cosmology and beyond. The research aims are to develop novel inference frameworks for cosmology and apply them