82 phd-mathematical-modelling-ecological-modelling PhD positions at University of Groningen
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model of human working memory. During the project, we will follow an intertwined model-experimental approach. First, we will use electroencephalography (EEG) experiments to investigate the neural basis
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consortium comes in. PRELIFE unites experts across a wide range of disciplines from astronomy, biology, chemistry, computer science, earth and planetary sciences, education, mathematics, to physics. Together
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PhD position - Modelling the emergence of information transfer in prebiotic self-replicating systems
consortium comes in. PRELIFE unites experts across a wide range of disciplines from astronomy, biology, chemistry, computer science, earth and planetary sciences, education, mathematics, to physics. Together
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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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Organisation Job description The Center for Language and Cognition Groningen is seeking a motivated PhD candidate in the fields of linguistics, computational linguistics, language acquisition, and
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We are looking to hire one PhD student within the European Union Project LIFE IP GrassBirdsHabitats (www.grassbirdhabitats.eu) to work on a research project on Agro-ecological rice systems
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and other stakeholders to gauge and optimize the ecological and bioeconomic consequences of North Sea OWF expansion. The announced PhD position focuses on foraging and life-cycle ecology of fish and
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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We are looking to hire one PhD student within the European Union Project LIFE IP GrassBirdsHabitats (www.grassbirdhabitats.eu) to work on a research project on spatial and foraging ecology of Black
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create