133 machine-learning-and-image-processing-"RMIT-University" Fellowship positions in Norway
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), stereo or high-speed imaging, and/or free-surface flows (including waves). However, an excellent candidate with a strong general experimental physics background can often learn quickly – the ability
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interdisciplinary team will develop a machine learning based monitoring system that leverages spoken language processing (SLP) and natural language processing (NLP) of speech recorded at home to calculate relapse
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learning frameworks previous experience in natural language processing, sound-based machine learning, development and deployment of health technology software . interest and previous experience in
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disciplines, including human-robot interaction, robot learning, soft robotics, computer vision, and agricultural robotics. About the PhD project: We are looking for a highly motivated and talented PhD research
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machine learning. Magnetic Resonance Imaging. Laboratory experience from porous media research related to physics and/or chemistry. Personal and relational qualities will be emphasized. Motivation
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be proficient in both written and oral English. Experience from one or both of the following areas is an advantage: Modelling and simulations of flow in porous media. Programming, image processing and
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questions related to the molecular regulation of autophagosome formation, using cell biological, genetic, and imaging-based approaches. The candidate will explore the function and regulation of proteins
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live-cell imaging of mitochondria in plants, algae, and marine metazoa with computational analysis to find the universal principles of mitochondrial motion across these species. The project is part of
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, we expect machine learning to be employed to improve accuracy and efficiency of numerical methods, combining advanced technology with scientific research. About the Department of Mathematics at UiB
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that mitochondria across a diverse range of other species might face the same pressures. This project will explore this hypothesis, combining live-cell imaging of mitochondria in plants, algae, and marine metazoa