12 algorithms-"DIFFER"-"NTNU---Norwegian-University-of-Science-and-Technology" positions in Norway
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of Oslo. via Unsplash Main responsibilities The position is fully embedded (100%) within SmartForest. The main responsibilities include: Developing supervised deep learning algorithms for 3D laser data
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different AI systems and services Decision support services to help educational decision makers (e.g. principals and government officials) ask creative and critical questions about the ways in which AI tools
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methods for developing games and web services that can be used to support parents, teachers and students to understand and compare different AI systems and services Decision support services to help
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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algorithms. Analyzing results and contributing to scientific publications in peer-reviewed journals (expected 3 papers), presenting at meetings and international conferences. Taking part in the teaching as
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. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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algorithms. Analyzing results and contributing to scientific publications in peer-reviewed journals (expected 3 papers), presenting at meetings and international conferences. Taking part in the teaching as
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological
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complex geometries represents a hazard. It is also relevant to compare the SoK for similar systems using different fuels, and to explore the predictive capabilities of consequence models through blind