72 parallel-computing-numerical-methods-"Simons-Foundation" Postdoctoral positions at University of Washington in United States
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computing and cloud-based infrastructure. A state-of-the-art UW Fiber Lab for DAS data and Pacific Northwest Seismic Network specialists in multi-sensor networks An working environment with a commitment to
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implement new computational and statistical methods. Create, test, and use relevant computer code (R, Python, SQL or equivalent). Maintain, modify, and execute analytic machinery that results. Draft
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Position Description The Department of Earth and Spaces Sciences at the University of Washington seeks a Postdoctoral Scholar to work on numerical simulations and data analysis to inform the search for life
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the data repository and computer servers. Runs existing PET/MR brain image processing pipelines on the computer servers, produces the results, and communicates with the group members. Writes computer codes
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methods for the early diagnosis, treatment, and prevention of pregnancy complications. The chosen candidate will play a critical role in advancing research projects and ensuring the successful completion
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epidemiologic methods for environmental ‘omics and related software packages, including R, SAS, Python or equivalent. Previous experience analyzing large DNA methylation datasets, bioinformatics, and use
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conditions, and brain tissue microstructure and functioning. The successful candidate will be working within a multi-disciplinary team of MRI physicists, computer scientists, radiologists, neuroscientists, and
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, Communication Sciences, Psychology, Neuroscience, Engineering, Computer Science, or a related field is required. 2. Strong analytical skills and experience with programming language(s) (MEG analysis in our lab is
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group. The successful candidate will have experience with paleobotanical methods, preferably including fieldwork, laboratory work, and statistical analysis. Experience with phytoliths is an advantage but
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the