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Field
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models that contribute to our understanding of this rapid transformation. The research focuses both on firm strategic responses and investment (supply-side), as well as consumer behavior and welfare and
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entanglement distribution in various topologies and its impact on network scalability; (iv) the simulation of the developed methods and techniques, along with the extraction and analysis of results; and (v
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solutions. • Topic (tasks): Creation of LCA and TEA models on technologies for the decarbonization of ammonia production; Providing recommendations to decision makers based on modeling results; Collaborations
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Infrastructure? No Offer Description Mission: Study of cell adhesion and tissue shape change with theoretical and computational models. Functions to be developed: Develop theoretical models. Implement these models
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required: The applicant should have a PhD degree in Economics with a solid background in quantitative methods (microdata and macroeconomic modelling) and programming, particularly with the R software
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and chemical evolution modelling Experience in galactic dynamics and analysis of numerical simulations Experience in programming in R, Python and/or SQL Experience in development of data analysis
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effectiveness and toxicity of the treatments. Other duties: Develop and validate cancer risk prediction models using deep neural networks based on semistructured data. Develop and validate learning strategies
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simulations. Where to apply E-mail lucas.perez@ucm.es Requirements Research FieldPhysics » Solid state physicsEducation LevelPhD or equivalent Skills/Qualifications Publications in spintronics. Experience in (1
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experiments and interpret results. Contribute to biomarker discovery and predictive model development. Support data visualization, reporting, and dissemination of findings in publications. Requirements PhD in
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learning) applied to operational infrared images. Design and test a drone payload for the detection and geolocation of fires in night conditions. Collaborate with modeling teams to integrate observational