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optimally integrated into a geothermal data-assimilation workflow. Where to apply Website https://brgm-recrute.talent-soft.com/job/job-phd-in-computational-geosciences_4… Requirements Research
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data. The development of these methods is motivated by a concrete and important application: inferring gas
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colleagues from diverse backgrounds and perspectives, and promote a healthy work-life balance for both yourself and others. Eligibility requirements The evaluation of the ability and potential to assimilate
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- data modeling and assimilation towards experimental measurements under consideration of uncertainties - utilization of Explainable AI techniques to enable novel scientific discoveries - explore and
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the MediTwin project, which aims at advancing patient-specific digital twins for medical applications by combining physics-based modeling, data assimilation,and efficient computational pipelines (https://www
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approaches. The research will also design a European-scale turbidity and sediment monitoring framework combining in situ observations and satellite remote sensing. Where to apply Website https://jobs.unibas.ch
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modelling techniques and the embedding of such models within data assimilation frameworks to enable their self-correction. During this project, you will develop expertise in machine learning-based reduced
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information about doctoral studies is available on the department’s website: https://www.iko.lu.se/en/research/doctoral-studies The recently established research environment “Radiation Safety and Society
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
. This involves theory but also the integration of observational information into models through data assimilation and model inversion. In this domain as in many fields of applied science, researchers face high