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at Aalborg University, Aalborg campus, and become part of the Cracks in Composite Structures (CraCS) research group. The research group focuses on the predictive modeling, characterization, and quantitative
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are software representations of physical assets, processes, or systems. They leverage real-time data to mirror the behaviour and characteristics of their physical counterparts, enabling predictive maintenance
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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implementation or comparable tools. Experience with one or more relevant methods, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy
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will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing computed models and predictions to be tested directly against advanced experimental characterisation
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machine learning to rich molecular data and uncover the molecular basis of IBD. The successful candidate will build prediction models from high-dimensional molecular measurements, with a particular focus on
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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leakage and the project ACE Water, in the topic degradation in LTEL and HTEL stacks and developing lifetime models, life-time predictions and the validation of them. You will be expected to contribute
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role in developing and implementing novel computational models within a boundary element framework, coupling the BEM with models for flow separation, viscous drag and cavitation. There is room to shape
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analysis, while other competence areas include e.g. causal inference, latent variable modelling and prediction models. The section aims to maintain a strong position in these areas but also wants to expand