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of the selected candidate, budget availability, and internal equity. Pay Range: $80,000-95,000 The Alsentzer Lab at Stanford is seeking a postdoctoral fellow to advance trustworthy, deployable AI methods
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substitution in the EGFRvIII peptide significantly increases survival in an animal model of glioblastoma by enhancing proteasomal processing. We also developed robust methods to detect a new class of non
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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
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combinatorial panning methods, including phage and mRNA display, to identify de novo peptides for promising biomarkers lacking a natural ligand or lead structure. We then optimize peptide ligands for affinity and
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training and experience; Proven knowledge in field of research; Experience in a variety of research techniques/methods and follow-up data collection; Knowledge of research procedures and protocols gained
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statistical methods and data analysis. Possibility to participate in other SCEC research projects and contribute to the preparation of future grant proposals. Required Qualifications: Ph.D. (or expected
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for Biomedical Informatics Research at Stanford University. This position emphasizes conducting real-world evidence studies using various causal inference methods (e.g., target trial emulation) to examine
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given to candidates studying early China using analytical methods such as zooarchaeology, paleobotany, ceramic analysis, and lithic analysis. The successful candidate will be expected to: Teach one course
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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radiolabeling of the resulting constructs. The fellow will conduct interdisciplinary research to develop unique translational therapeutics or methods to quantify the imaging data. Our federally-funded team is