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Vacancies PhD Candidate Geospatial Risk Modelling for Climate Finance Key takeaways Effectively understanding and mitigating financial risks associated with climate change is important for
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properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a
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programming and modelling complex (biological) systems, and in interpreting model output in terms of its biological meaning. You have strong collaborative skills and want to contribute to the project as whole
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. The focus involves expertise in energy modeling, optimization and analyzing diverse energy mixes, primarily emphasizing hydrogen-based systems. As we move toward cleaner energy systems, the way we plan and
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PNRR, in particular in the field of model-based systems engineering (MBSE) and the development of a model-based digital twin facilitating a fast and rigorous requirements verification process within
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) to develop computational models for electric breakdown of gases Job description Electric gas discharges occur in nature, most prominently in air in the form of lightning and its less visible precursors
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to train an AI model that predicts the cis-regulatory code for synthetic genomes (i.e. for cell-free gene expression systems) and correlates the experimental conditions within the synthetic cell
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models combined with the finite element method. Constitutive relations are required to describe material behavior. Advanced stainless steel typically possess complex microstructures across various length
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models combined with the finite element method. Constitutive relations are required to describe material behavior. Advanced stainless steel typically possess complex microstructures across various length
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in the SPG. We will make use of models of different complexity up to complex Earth System models, and modelling efforts for different past periods. A personalised training programme will be set up