58 postdoc-in-thermal-network-of-the-physical-building Fellowship positions at University of Oslo
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to an ideal learing setting. The candidate will contribute to understanding how neural networks extract the most relevant information of the data to make a prediction using advanced mathematical tools
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PhD Research Fellow in Experimental Fluid Mechanics: Tunable hairy surfaces for droplet flow control
. The fellowship period is 3 years. The position is part of the HAIRY project funded by the Research Council of Norway. The project aims to provide a fundamental understanding of the physical processes involved as
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on bio- and/or geo-diversity be hosted by at least two of NHM´s research groups enhance and/or use NHM’s physical and digital collections, ideally both Expectations of the postdoc: Carry out the project
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. The fellowship period is 3 years. The project will provide a fundamental understanding of droplet flow on single and complex fiber networks. Essential to the project is the development of a new understanding of
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sematic technologies. Both groups have a dynamic and interactive working environment with good gender balance, consisting of full-time professors, researchers, and many postdocs and PhD candidates
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. The project aspires to elucidate the physics governing droplet impact and wetting on fibrous networks in order to enhance fog net technology. The planned work is experimental and will be conducted in
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candidate and availability of the pilot sites. The core activity of the ATLAST2 shall support Researchers, PhD candidates and Postdocs. They work in close contact with the research and user partners
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efforts to build real-time, operational early warning systems in collaboration with international part-ners and public health institutions. The position is based at the HISP Centre at the Department
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be possible, depending on the interest of the PhD candidate and availability of the pilot sites. The core activity of the ATLAST2 shall support Researchers, PhD candidates and Postdocs. They work in
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hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing efforts to build real-time, operational early warning systems in collaboration with international