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Cori Institute of Molecular and Computational Metabolism of the Austrian Academy of Sciences | Graz,12.Bez.:Andritz, Steiermark | Austria | about 2 months ago
, mathematical, engineering and translational scientists for two distinctive routes to scientific independence: CORI STARTING PRINCIPAL INVESTIGATORS CORI PAIRS , an innovative shared-leadership model for
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implementation of the modelling teams, and the cross-model synthesis comparing new pathways with the existing literature. Main tasks and responsibilities In the following items, we deliberately use
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transport model FLEXPART and studies all aspects of transport in the atmosphere, ranging from greenhouse gases to pollutants, water vapour and heat, and we use inverse modelling techniques to determine
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transport model FLEXPART and studies all aspects of transport in the atmosphere, ranging from greenhouse gases to pollutants, water vapour and heat, and we use inverse modelling techniques to determine
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Infrastructure? No Offer Description Start date/duration as soon as possible until 14.09.2027 Job profile: Your responsibilities: PhD thesis in the field of software engineering and model-driven
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Research Center for Molecular Medicine (CeMM), ÖAW | Graz,12.Bez.:Andritz, Steiermark | Austria | about 1 month ago
Investigators Cori Pairs , an innovative shared-leadership model for scientists who want to build something together This is not simply an opportunity to join an established institute. It is an
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transport model FLEXPART and studies all aspects of transport in the atmosphere, ranging from greenhouse gases to pollutants, water vapour and heat, and uses inverse modelling techniques to determine
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Geophysics. The group develops the Lagrangian transport model FLEXPART and studies all aspects of transport in the atmosphere, ranging from greenhouse gases to pollutants, water vapour and heat, and
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, coaching, and professional development opportunities Fair and transparent salary according to the University of Vienna collective agreement Model 1: The employment duration is 3 years. Initially limited to
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systems Efficiency: Streamlining large-scale model experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model