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PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys
fuels from recycled CO2 feedstock” funded by the Research Council of Norway. The project aims to advance fundamental insight into the structure-composition-function correlations that govern
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technological progress in our increasingly digital, data-driven world. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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the most relevant information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust
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) and economics (or related fields). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization Econometrics Virtual power plants Power systems and/or
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capillary flow, drop impact, and wetting in fibrous networks. The PhD research fellow will design droplet flow experiments on fibers and focus on droplet accumulation on fibrous structures when exposed to fog
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sciences or medicine, with a proven track record of cardiac research Previous experience with molecular cardiology, viral transduction, cell transfections, animal models, immunoblotting, qPCR, cardiomyocyte
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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian
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humans, for example in the case of sacred sites, while contributing to period conceptualizations and visualizations of land- and seascapes as objects of knowledge. The person filling in this position will
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information of the data to make a prediction using advanced mathematical tools. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms