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scales. The project involves the modelling of energy infrastructures, the development of scenario-based simulations, and the generation of actionable indicators to support decision-making. You will be part
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chemical–biological pathway. The advertised positions will support these efforts through research on advanced carbon capture methods, process modelling and optimization, and biological CO2 valorization in
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epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
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, for enhancing light trapping in nanostructured thin-film solar cells. Your role will focus on developing and applying large-scale electromagnetic simulations to identify optimal nanostructured light-trapping
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well as the residency programs and clinical training of Humanitas University, an international Medical School that stands out for its real-world simulation-based approach to medical training. Humanitas Research is made
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of millions of lakes worldwide. The successful candidate will create innovative solutions that significantly enhance large-scale environmental simulations and meaningfully advance the modeling of global lake
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(e.g., including simulation, prediction). Enable the evolution of integrated data/models all along the Digital Twin lifecycle Ensure the consistency of integrated data and models with regard to potential
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contexts. Additional experience in industrial ecology, material flow analysis, and in particular energy systems modelling, simulation, and mobility will be considered a strong asset. The candidate shall be
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-layer and cloud processes in the air-mass transformations that follow moist intrusion events into the Arctic. Using large-eddy simulation models, you will set up and run case studies of observed air-mass
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resilience and its change over time in the past (based on Earth observation data), present and future (based on Earth system model simulations for different future scenarios, e.g. using the CMIP6 ensemble and