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: OUH - Cell and tissue dynamics (Bøe) Project description GENESIS is a newly established Life Science Convergence Environment that brings active matter physics, cell biology, and machine learning
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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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datasets from laboratory experiments will be provided to support simulation and verification of the resulting model. Replicate and learn a theoretical model for wave and current interaction by posing
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modeling; performing source separation on commercial recordings and extracting audio features (onsets, pitch, harmony, dynamics); curating datasets; and integrating machine learning approaches to complement
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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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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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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
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, corporations and the international order; on the development of political and military strategy; on how the wars were prepared, experienced and remembered; and on how peace was made. BLOCKADE sets out to prove
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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