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both technical insight into data modeling and a solid understanding of how real-world engineering data is generated, structured, and used. We are seeking motivated candidates with strong programming
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a team of 25 colleagues dealing with CCUS, and industrial partners in Denmark as well as abroad. Your primary tasks will be to: Apply AI in context of capture solvent modelling Understand and analyse
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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. The position will involve development of codes/models simulating the nucleation and propagation of stress corrosion cracking in samples under low cycle fatigue conditions, as well as models for linking
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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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-cutting and bending to break the glass panels. The project will involve the establishment of a numerical model and the acquisition and analysis of data from physical measurements in the production
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including