123 parallel-computing-numerical-methods research jobs at University of Washington in United States
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or experience in nontraditional research publication methods and collaborative notetaking software (e.g., Roam Research, Obsidian, Notion). ? Familiarity with cloud computing and machine learning techniques
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to infectious patients and specimens. • Will spend long periods of time working at a desk on keyboard/computer. • Will work in a high-traffic area that will require working with numerous people. • Will work in an
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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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-of-use water filter during use. Methods are expected to use LC-MS/MS and GC-MS workflow. These methods would be applied for laboratory testing and in a pilot testing program. Job Description Primary Duties
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Scheduled Hours 40 Position Summary We are seeking a highly motivated individual who can work both independently and collaboratively with a passion for advancing MRI methods and applications. Our
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disease with a focus on Alzheimer’s Disease. Computational models will be developed that utilize data obtained from a wide range of experiments, from basic biochemical methods to advanced imaging techniques
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, 40%) at WashU. This position focuses on spatial transcriptomics, supporting research in vascular and brain tissues. We seek candidates with a strong background in statistical and computational methods
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to facilitate variant discovery, manage storage and transfer of large datasets, perform variant analysis, and assist the team in evaluation of new computational methods and tools. Job Description Primary Duties
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), Quantitative Methods, Rodents Questions For frequently asked questions about the application process, please refer to our External Applicant FAQ . Accommodation If you are unable to use our online application
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data sources to determine their relevance and utility for ongoing analysis. Understand key data sources and variations in these across and within countries. • Apply computational and statistical methods