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Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic modelling of materials and machine learning. Experience in atomistic modelling (molecular
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-proof. You will contribute to the CYCLIC project : Cyclic Structures in Programs and Proofs , a collaboration among five Dutch universities, uniting experts in cyclic structures, coinduction, program
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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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, climate sciences, mathematics, or geo-information science or a similar relevant field, and are you proficient in programming with R/Python? Then we have the perfect opportunity for you! The Team
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PhD in Data-Driven Modeling of Homogeneous Catalysts (1.0 FTE) (V25.0035) « Back to the overview Job description Among the most challenging to develop catalytic reactions are stereoselective
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regarding the demand that will have to be met soon. This requires building robust models to predict future demand, making decisions under time heterogeneous uncertainty and adapting to the state
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dynamic environmental conditions; Integrate empirical data analysis into conceptual models to investigate how altered conditions due to offshore wind farms may lead to broader ecological impact across
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-year PhD program, you will join a collaborative research team applying cutting-edge methods from Experimental/Behavioral Economics alongside modern macroeconomic modelling techniques (e.g. DSGE). You'll
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energy system modelling is critical for reducing emissions, enhancing resilience, and integrating high shares of renewable energy sources. Such modelling provides essential tools for designing low-carbon
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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