117 parallel-and-distributed-computing-phd-"Multiple" Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
lab in Stanford’s Psychiatry Department, led by Neir Eshel, MD, PhD. We are looking to hire curious and ambitious postdocs to join our team. Lab projects focus on the neural circuitry of reward-seeking
-
external) Candidates from a diverse background are encouraged to apply. The applicant may hold a PhD either in physical sciences/engineering with a strong interest in translational research and motivation
-
postdoctoral fellowship is available in the laboratory of Jeffrey Goldberg, MD, PhD in the Department of Ophthalmology, Stanford University School of Medicine. Dr. Jeffrey Goldberg is Professor and Chair
-
Qualifications: The Stanford Energy Postdoctoral Fellowship is open to individuals who: 1. Will have been awarded their PhD within the last three years. The PhD must be conferred by the start of the award term. 2
-
success and publication history, with an MD, PhD or MD/PhD degrees, and very strong references. We are seeking a candidate with expertise in immunology. Previous experience in cancer research, molecular
-
Postdoctoral position in Computational Immunology We are looking for two motivated postdoctoral researchers to work on human macrophage biology in the Department of Pathology at Stanford. Successful candidates
-
with a strong background in cognitive or computational neuroscience, with an emphasis on neuroimaging techniques and computational methods. The ideal candidate will possess not only a deep conceptual
-
clinicians at Stanford University as well as other institutions. Required Qualifications: Candidates must have a PhD or MD/PhD with expertise in immunology, cell, molecular, or developmental biology, and past
-
at conferences and publish results in peer-reviewed journals. Support mentorship of junior researchers and/or students. Required Qualifications: PhD in Computational Organic Chemistry or Computational Materials
-
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