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Field
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validation methods employed for battery systems created for electric vehicles, aerospace or stationary storage. This PhD will aim to deliver a new validated methodology for scientifically assessing lithium-ion
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, demand and supply values at these nodes are subject to uncertainty, which complicates logistics planning and decision-making. To tackle this challenge, we aim to develop efficient data-driven methods
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, or geometric deep learning. Experience with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage
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spanning a broad range of research areas in biostatistics, machine learning and epidemiology and numerous collaborations with leading bio-medical research groups internationally and in Norway. OCBE is a
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with optimization methods, numerical modeling, or simulation of complex systems. Experience with 3D modeling, CAD APIs, or computational geometry is an advantage. Experience and abilities
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systems. By combining microclimate modelling, remote sensing data, and data-driven methods, the results are integrated into a Digital Twin framework. The research will support predictive risk assessment and
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Engineering, or a related discipline with substantial background in fluid mechanics. Essential skills: • Strong knowledge of numerical methods and fluid mechanics • Experience with scientific
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, ultimately optimising the deposition process. Additive manufacturing (AM) is a rapidly advancing technology, driving numerous innovations and finding diverse applications across industries such as aerospace
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to the development of non-destructive methods to monitor natural degradation, using advanced analytical and statistical approaches. • Develop, implement, and refine models that integrate micro-environmental
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methods from building physics, you will bridge the gap between room-scale dynamics and micro-environments of documentary heritage objects, translating complex simulation data into actionable conservation