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models implementable on digital infrastructure (e.g. cloud computing). The work will involve active engagement with various consortium partners in a fast-paced R&D process, as well as encompass extensive
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will work with PhD students and a fellow postdoc. You will also become an integral part of the Multiscale Cloud Physics Group currently being established by Dr Franziska Glassmeier at the Max Planck
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applied machine learning projects in, e.g., computer vision, in close collaboration with industry partners. The position is not connected to an existing project, so the postdoc fellow will either join an
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system involving end-users, data content distribution, computing services and their interactions with the edge-cloud infrastructure and data indexation system.- Experiment with such a system using the
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) to conceptualize and quantify the controls of anvil extent and cloud feedback of tropical deep convection across scales. As a successful candidate, you will combine your expertise in deep convective processes with
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Positions: two postdoc positions in the development of electrodes materials for ultrahigh performance of metal-ion batteries via advanced multi-scale computational modeling About UM6P: Located
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cloud computing and modern data science tools to analyze high-dimensional, time-resolved data from clinical environments. You’ll collaborate with faculty in AI, clinical informatics, and emergency
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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications
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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications