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Field
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networks, natural language processing (NLP), and multimodal learning (ML). Independently read and critically evaluate existing literature on NLP and ML. Implement neural networks and carry out experiments
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of nonlinear optics through the integration of artificial intelligence. The successful candidate will lead projects that combine the development of Physics-Informed Neural Networks (PINNs) with advanced fiber
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extensive experience of python programming, deep neural network analysis, and large language models. Ability to work independently and generate innovative, out-of-the-box research ideas. Ability to work
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who are interested in these or related fields, particularly those who may bring a new technology or perspective to bear on the work in the lab. Familiarity with neural networks and/or primate
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develop numerical models and Machine Learning and AI methodologies, including Physics Informed Neural Networks (PINNs) and Symbolic Regression tools, to predict chemical reactions, impurity evolution along
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). The position is subject to financing by the University of Bergen. About the project/work tasks Geometric Deep Learning (GDL) is a branch of machine learning that develops neural network models by explicitly
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within a Research Infrastructure? No Offer Description Work Plan Study and application of methods for extracting understandable concepts and inducing logic-based theories from neural networks. Study of
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, including deep neural networks and physics-informed neural networks, to analyse large datasets from gyrokinetic and fluid simulations of plasma turbulence Develop and train reduced-order models that capture
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, Training or work experience in medical imaging, digital image processing, computer vision, pattern recognition, artificial intelligence, machine learning, deep neural networks, and statistics, Hands
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neurophysiological experiments - mathematical analysis of the dynamics of neural networks - programming and numerical simulations of neural networks - development of quantitative model predictions and