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Field
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links atomic-scale chemistry, mesoscale transport, and device-level performance, allowing researchers to test, predict, and optimize designs in a computer before building them physically. By improving
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
and verbal communication skills; Ability to work effectively in multidisciplinary environments and as part of a team; Scientific rigour and autonomy; Excellent command of English, both spoken and
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of Engineering. Where to apply Website https://www.jobbnorge.no/en/available-jobs/job/304536/phd-in-predictive-ai-base… Requirements Research FieldEngineeringEducation LevelMaster Degree or equivalent Additional
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develop predictive tools for mechanical failure. Our team is highly interdisciplinary and international, bringing together researchers with backgrounds in materials science, mechanics, and applied physics
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objective is to develop methods that move beyond correlation-based prediction toward causal reasoning, intervention-aware modelling, and interpretable AI systems. This transition from correlation to causation
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expulsion compared to atmospheric CCDS welding. From the metallurgical side, the absence of air yields a more controlled and predictable chemical environment, yet intermetallic compound (IMC) formation in
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tissular scales in live developing, crustacean embryos to elucidate the control mechanisms that establish their bilateral symmetry during normal embryogenesis and restore it during healing after
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position This PhD project is connected to FME NorthWind (https
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scoring and targeted intervention of friability, (ii) reduce suspension line development timeline through prediction of optimal growth and elicitation conditions, and (iii) consolidate results into standard