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environments. Experience with parallel computing environments, HPC in a Linux environment. Experience with surrogate modeling. Experience with data analytics techniques. Familiarity with C++ and GPU programming
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the NIH, Department of Defense, the Gates Foundation and private industry. A parallel search is underway for a postdoctoral fellow for candidates who do not yet have at least two years of post degree
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and computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
. • Is motivated to develop new methods and biological insights in parallel. Especially Excited If You Have • Experience with bacterial genetics, functional genomics, or drug-response assays. • Familiarity
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system control and simulation including turbulence compensation Awareness of high-fidelity flow simulation methods, actuator-disc methods, and turbulence modeling approaches Experience with parallel
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computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other
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of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral rank are for one year with the possibility
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neurons using multi-omics, cis-regulatory variants will be mapped onto these enhancers and then their function determined by Massively Parallel Reporter Assays (MPRA). The research will involve stem cell
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attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O. Conte Center on the "Cognitive Thalamus". The successful
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns