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software (FSL, SPM, or Afni) and relevant statistical analysis approaches. Experience with programming languages Matlab, R and/or Python. Experience with big data analysis. Experience with Microsoft Office
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to engage in pioneering research, collaborate with a large, dynamic and multidisciplinary team, and advance the field of quantum computing through innovative algorithms and technologies. This is an exciting
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clinical trial portfolio for a research programme in antimicrobial resistance and work to support colleagues in data-enabled trials. You will also undertake statistical methods research associated with
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that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging,” and we love
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and cellular biology, assist in sorting platform development, perform molecular and in vivo characterization of sorted cells, analyze large-scale bioinformatics datasets to identify novel cell subsets
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the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under-actuated large-scale
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using large language models, disease endpoint coding initiatives, and creation of common data model (CDM). He/she will also be expected to support/lead high-quality research in chronic disease
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that address real-world challenges and deliver positive business outcomes. The Institute for Insight is equipped with a computer cluster that includes multiple GPUs, designed for big data analytics for both
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Research Assistant, Ifergan Lab, College of Medicine, Department of Molecular & Cellular Biosciences
education (co-op) program in the country with students earning $88.8 million annually through paid experiences, an academic health system, and as a member of the Big 12 Conference. The university contributes
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. The project will define new near miss and severe morbidity definitions allowing us to identify electronically when significant events happen. We will then develop a large multi-centre maternity routine dataset