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Hospital, and with experts in mathematical modelling at the translational research hub IceLab , Umeå University. Work responsibilities As a postdoc in our team, you will be an integral part of a dynamic and
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constrained environments - Data-driven and learning based control - Meta-learning for emergent behavior in multi-agent systems - Learning-based cognitive awareness, contextual and neuro-symbolic reasoning
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improve models and codes for assessment of the liquid source term during severe accident (SA) of LWR systems. In particular, research will include assessment of the characteristics of the containment water
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motion planning - Predictive collaboration with heterogeneous teaming - Multi-agent navigation in constrained environments - Data-driven and learning based control - Meta-learning for emergent behavior in
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remote brain areas in vivo in animal models that underlie plasticity in the prefrontal cortex of mice. We will focus on neuronal micro- and macro-circuits that influence our feeding behavior, which
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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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ecosystem functioning. The postdoctoral project will focus on analyzing and modeling tree architecture in relation to forest vitality and stress predisposition. The work will combine 3D structural data (from
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research is carried out in collaboration with industry and focuses primarily on the development of environmentally sustainable pest and disease control strategies for agriculture and horticulture, both in
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mathematics, ecology, history, climatic and medical sciences in collaboration across multiple institutes. An integral part of the project is to develop process-based eco-epidemiological models considering
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relevant for the postdoc project. - previous knowledge of exploring datasets on forest production, carbon cycles and/or biodiversity -very good command of the English language, both written and spoken