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the United States, or must have been lawfully admitted for permanent residence (i.e., in possession of a current, valid Alien Registration Receipt Card I-551, or must be in possession of other legal verification
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particular, the postdoc will focus on applying reinforcement learning to discover vulnerabilities and failure modes in software systems that support critical infrastructure, in particular AI-based decision
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of other legal verification of such status); individuals on temporary or student visas are not eligible not be supported by any other NRSA grant at the time of the T32 appointment Trainees must have received
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): Computer Science or Informatics: Proficiency in programming and software development with a habit for robust unit testing. Our group mainly develops software in a Python + SQL environment with use of large language
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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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excellent benefits and competitive salaries. Support is available for 1-year appointment with renewal up to 3 years of training and includes: Tuition, books, and software; Research-related costs; Conference
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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. Demonstrated experience with large
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peer-reviewed journals, contributing to open-source software, and presenting at leading scientific conferences, is essential. The successful candidate is expected to take full advantage of Stanford’s
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.) and proficiency in data analysis software (such as R, Mplus, SPSS). Extensive experience interpreting research data and summarizing findings via written reports and oral presentations. Strong record
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implement new paradigms from hardware to software components, including virtual reality, probe mounting/registration. Work together with other teams of the Enigma Project to ensure efficient, large-scale