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, emphasising our commitment to widening opportunity through the power of education and contributing our talents to creating a fairer world, and a more prosperous region. We are putting inspirational learning and
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interfaces under reaction conditions, using machine-learned interatomic potentials (MLIPs) for automatic reaction network exploration for catalyst dynamics, and developing the next generation of kinetic Monte
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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sensing across telecommunications networks to deliver the performance level required by the multiple sensing applications. The applicants should have a solid theoretical background on machine learning
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maritime transport, marine technology, computer science, or a related field; Excellent programming skills, such as Python, Matlab, C++, or other computer languages; A record of publications in reputable peer
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cardiac precision medicine through artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and
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Are you an atmospheric scientist looking to apply your expertise to real-world forecasting challenges in Africa? Machine-learning has the potential to revolutionise weather prediction in Africa, and
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and sensor systems for remote maintenance and reliable fusion operations. Artificial Intelligence Developing advanced artificial intelligence/machine learning (AI/ML) solutions for fusion science and
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and machine learning projects involving heterogeneous data. S/he is expected to collaborate and exchange with the previous experts involved in the project and to produce efficient work to achieve
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Norwegian courses. Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms