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students, postdoctoral fellows and other trainees. Qualifications Required Qualifications: PhD in Neuroscience, Immunology, Vascular Biology or related field An excellent track record of publications
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-constrained environments, including edge AI applications. Guide PhD students and collaborate with engineers to implement advanced ideas. Author scientific publications, present findings at conferences, and
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of advanced statistical methodologies, and supporting research on high performance and cloud computing. The successful candidate will also be expected to offer 2-3 advanced technical or methodological workshops
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processes to developing weather forecasting and climate projection systems for Météo-France's operational services. The postdoctoral researcher will collaborate with a PhD student working on a complementary
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School has three campuses. A four-year MD program and the MD/PhD program are located on the Twin Cities campus in addition to MD programs at regional campuses in Duluth and St. Cloud. Apply for Job
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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LevelPhD or equivalent Skills/Qualifications Requirements: PhD or MSc in a relevant field of interdisciplinary research (e.g. Bioinformatics or Computational Biology). R software development experience
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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interpersonal skills. Proven leadership skills. Minimum Qualifications PhD in Computer Science, Software Engineering, or a closely related discipline. Must have PhD conferred upon hire. Preferred Qualifications
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at scientific edge systems using large-scale HPC/AI computational and storage systems. Design and evaluation of ephemeral, user-configurable, and composable data and storage systems. Evaluation of cloud data