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behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model species, the PhD student selected for this project will investigate unanswered questions on how complex
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inherent complexities. Your role: The doctoral student will conduct research at the intersection of optimization, game theory, and automatic control for complex systems. Their work will encompass both
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studies within the Marie Skłodowska-Curie Actions (MSCA ) funded network program for PhD students, IDEAL4GREEN , that addresses the urgent challenges of climate change and the global shift towards
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for large-scale simulations of cortical memory function. Special focus is on coupling multiple neural networks to study neural interactions between different cortical regions supporting cognitive function
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northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering
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control strategies that strengthen their disturbance-handling capability. The work will be carried out with in a MSCA-DN doctoral training network with research stays at DNV in the Netherlands, Aalborg
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. The research team focuses on developing novel methods to extract knowledge from data, modeling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but
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advanced methods in AI and machine learning, combined with atomistic spin dynamics and first-principles electronic structure calculations, to study complex nanomagnetism. A specific goal of the project is to
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(CFD) of new solutions for capturing biogenic CO2 from point sources. The work will consist in creating simulations tools based on CFD to simulate details on CO2 capture by solvents in complex geometries
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constraints, scarce data, and high variability. In particular, there is a need for a better understanding of how embodied cognitive agents can learn to solve complex problems and adapt in dynamic real-world