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probes. The postdoc will play a key role in designing and optimizing imaging devices, integrating hardware and software, and collaborating with a multidisciplinary team of clinicians, engineers, and basic
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. Oversee enumerator training, pre-testing, and quality assurance protocols. Code survey into appropriate software (e.g., SurveyCTO, RedCap). Conduct field monitoring and troubleshoot real-time data
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the robustness to address national security challenges in cybersecurity. In particular, the postdoc will focus on applying reinforcement learning to discover vulnerabilities and failure modes in software systems
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and machine learning based software to assist clinical workflow and pre-clinical studies. Recent software developed from the group has been adopted in the clinic and preclinic labs. The scientific
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possession of other legal verification of such status) and not be supported by any other NIH grant at the time of the T32 appointment. Individuals on temporary or student visas are not eligible. Required
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written
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ability in a diverse sample of children with dyslexia and typical readers. Scientific reproducibility and transparency: Our team has a long history of developing open-source software to support rigorous
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/ljiYqBbnJkOn3jp2EpXY6g/project-details/10720073#description (link is external) (4) Develop, deploy, and evaluate software systems and data analytics to improve inpatient hospital care value efficiency. For example
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skills in statistical software (e.g. R, Stata, Python) and working knowledge in SQL Excellent written and oral communication skills Strong record of distinguished scholarly achievement, including written
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred