125 software-verification-computer-science Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
, 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
-
Posted on Mon, 08/04/2025 - 11:14 Important Info Deprecated / Faculty Sponsor (Last, First Name): Wolak, Frank Stanford Departments and Centers: FSI Program on Energy and Sustainable Development
-
backgrounds trained in chemistry, chemical biology, microbiology, and/or biophysics fields. We have launched a collaborative antibacterial drug design program integrating chemical biology and mechanistic
-
immunologic skin diseases. Candidates are welcome from various interrelated backgrounds, such as epidemiology, computer science, public health, health services research/health policy, and/or biostatistics
-
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
-
): 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
-
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
-
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
-
technical skills in statistics, data science and psychometrics Experience with open-source software development (or high proficiency) in R or Python Domain knowledge in reading development, dyslexia research
-
include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance