95 high-performance-computing-postdoc positions at Rutgers University in computer-science
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oversight of high-performance computing (HPC), scientific instrumentation support, enterprise applications, and cloud-based platforms. The Manager provides direction for infrastructure services that support
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support services for all research technologies and systems, including server infrastructure, scientific instruments, high performance computing, AI, and cloud computing infrastructure and services
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to the applicant’s academic rank. Candidate must possess good understanding of informatics infrastructures to support highly innovative research within high performance computing (HPC), cloud computing, and secure
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include oversight of high-performance computing (HPC), scientific instrumentation support, enterprise applications, and cloud-based platforms. The Manager provides direction for infrastructure services
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perturbations, including changes in gene expression, enzyme activities, cellular phenotypes, and metabolite production; and 3) performing statistical analyses of human clinical and/or omics data to evaluate human
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, and support services for all research technologies and systems, including server infrastructure, scientific instruments, high performance computing, AI, and cloud computing infrastructure and services
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Minimum Education and Experience: Ph.D., MD, MD/PhD, or equivalent terminal degree in Data Science, Bioinformatics, Neuroscience, or a related field. A research program with currently active federal funding
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Qualifications Five years of related experience in instructional design, curriculum development, or preparing content for training programs. High level of technological literacy and must be familiar with at least
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. We encourage those to apply who are dedicated to undergraduate education using approaches that capture the current body of students, while maintaining the expected high level of education provided by
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include computational biology and/or biomedicine, multi-modality fusion and inference (visual and NLP data), computationally efficient ML, environmental sciences, business applications, chemistry, and