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Danfoss, you will combine thermofluid modelling, reduced-order multiphysics methods, and nonlinear rotor dynamics analysis to develop predictive modelling tools that support the industrial design of
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behaviour for system monitoring, state estimation, and performance prediction. Build computationally efficient models suitable for monitoring, performance prediction, optimization, and control, and evaluate
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strengthen explanatory and predictive models of surveillance data. Job Description and Research Objectives At the Capital Region of Denmark, Nordsjællands Hospital, Region Hovedstaden, DETECTIVE is looking
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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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technologies Development and utilization of high throughput methods for characterizing and quantifying the physicochemical behavior of food macromolecules in complex matrices. Modelling and the use of machine
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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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in-line or at line spectroscopic tools Large-data, modelling and prediction of food and ingredient processes. Understanding of biological production systems and their use, and with an emphasis
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reliably assess fatigue damage and predict remaining lifetime, enabling proactive maintenance strategies that extend service life and reduce CO2 emissions. Through this PhD scholarship, you will contribute
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is the development of hybrid models that combine our integrated national model of Denmark with AI-based methods to improve predictions of floods and droughts. We seek to enhance our ability to predict
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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