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curiosity, openness, and intellectual humility, with an emphasis on constructive communication and shared learning. We support individual development while working together to achieve ambitious research goals
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information and communication theory, machine learning, and signal processing. We offer a dynamic, supportive, and international research environment with around 150 employees from more than 20 countries. Our
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. Experience in Atmospheric Water Harvesting, metal organic framework modelling and machine learning is highly desirable. An ideal candidate will have a solid background in physics and chemistry, particularly in
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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, required to adequately incorporate molecular data, and model regulations of inflammatory and degenerative processes. Available datasets at the molecular level will be incorporated through machine learning
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machine learning methods. Provide theoretical predictions to guide experiments, and atomic-scale physical understanding to experimental observations. Publishing findings in peer-reviewed journals
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aspects of machine learning and deep neural networks Free Probability aspects of Quantum Information Theory. While excellent candidates with other research interests might be considered, priority will be
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for the Science of Learning & Technology (SLATE), Faculty of Psychology there is a vacancy for a postdoctoral research fellow position within artificial intelligence and education. The position will also
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biology-experimental and/or theoretical biophysics-experimental and/or computational genomics-computer science, statistics, and/or machine learning with applications relevant to genomics-bioinformatics
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital