111 high-performance-computing-"Multiple"-"Prof" Postdoctoral positions in United States
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program including development and review of research guides. Support the Libraries’ Key Performance Indicators (KPIs) and other strategic initiatives. Support efforts to maintain R1 and obtain Top 100
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closure modeling and/or high performance computing environments (MPI, CUDA) • Expertise in software development and computing tools (C/C++, python, git, parallel computing, etc.) • Experience with deep
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to the performance of high-power accelerators such as the Spallation Neutron Source (SNS). The goal of this work is to demonstrate predictive modeling of beam distributions in a realistic accelerator, and as such
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Research Associate position, with the possibility of extension for up to two years based on an annual basis dependent upon satisfactory performance and the availability of funding. The candidate will be
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editing. The individual will be expected to hold high standards in how information is developed and presented. In addition, this position will require the ability to manage, document, and organize multiple
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will have the opportunity to develop innovative algorithms and models that integrate multiple data modalities, collaborate with industry partners, and contribute to high-impact publications. Job
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conduct experiments aimed at understanding tumor-immune interactions in patient-derived glioblastoma organoids and other preclinical brain tumor models (30%). Data Integration and Analysis: Perform
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Benefits Rutgers offers a comprehensive benefit program to eligible employees. For details, please go to http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary The Rutgers Center for Advanced
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commensurate with their essential status should be afforded, independent of the postdoc's source of funding. Responsibilities: Perform advanced computational analyses on longitudinal brain imaging and behavioral
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goal of informing public health policy. The postdoctoral scholar will have the opportunity to lead high impact research in infectious disease epidemiology and modeling across multiple pathogens. The work