221 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" positions in Norway
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resilience of bridges under climate change-induced hazards such as flooding, scour, and debris impacts. The research aims to develop advanced numerical models and machine learning tools to predict loads
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, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in
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within sanctioned boundaries. Underpinning both is the need for dependability models that, combined with telemetry-driven learning, can guide self-healing decisions in a way that reduces downtime without
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Technology (NTNU) for general criteria for the position. Desired qualifications Applicants should possess a basic understanding of key AI concepts (machine learning, neural networks, prompt engineering, human
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development on rhythm modeling; performing source separation on commercial recordings and extracting audio features (onsets, pitch, harmony, dynamics); curating datasets; and integrating machine learning
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limited to these topics, but preference can be given to candidates with a potential to contribute to the ongoing academic dialogue in the department. Applicants must be able to teach in Norwegian or a
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public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest in creative or artistic applications. Documentary evidence would be beneficial
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are also affiliated with the Norwegian Centre for Knowledge-driven Machine Learning (Integreat) . The candidate is expected to join Integreat and strengthen the interdisciplinary research on the boundaries
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(predictive) modelling, for example machine learning approaches. Experience in conducting field work in polar or alpine regions. Strong and preferably demonstrated interest in interdisciplinary work at the
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outcomes and economic performance, specifically addressing challenges such as overdiagnosis in cancer care. We will utilize economic theory, simulation, economic evaluation and machine learning to quantify