88 software-verification-computer-science Postdoctoral research jobs at Stanford University
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Planetary Health (HPH) (link is external) and Project Unleaded (link is external) for an exciting postdoctoral fellowship that contributes to a high-impact global program with a mission to create a
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University. The ideal candidate will have a strong background in engineering—biomedical, electrical, or mechanical—with expertise in optics, imaging systems, or device development. Our research focuses
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campus in linguistics, computer science, psychology, and otolaryngology. Applicants are expected to apply for independent funding through Stanford-internal mechanisms and/or external sources such as NRSA
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, California 94305, United States of America [map ] Subject Areas: Applied Physics Chemistry Materials Science Quantum Optics Computer Science (more...) Quantum Gravity quantum gravity/quantum cosmology Quantum
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, economics, computer science, operations research, or related data science fields. The position provides opportunities to participate in rigorous, quantitative research on human trafficking, including supply
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computer vision projects Experience in software or webapp development/API integration Interest (but not necessarily expertise) in medicine and radiotherapy Required Application Materials: Curriculum vitae 2
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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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will have connections to both the Molecular Imaging Program at Stanford (MIPS) and the Radiological Sciences Laboratory (RSL). The ideal candidate for this position will have interest in being trained in
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is $76,383. Are you looking for a challenging and rewarding postdoctoral fellowship in pain science, substance use disorders (SUD), or data science? Join the next generation of pain and SUD
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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