78 phd-in-computational-mechanics-"Prof"-"Prof" Postdoctoral positions at Stanford University
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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
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Posted on Mon, 08/04/2025 - 17:10 Important Info Deprecated / Faculty Sponsor (Last, First Name): Knowles, Juliet Other Mentor(s) if Applicable: Frank Longo, MD PhD Stanford Departments and Centers
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their knowledge and skillset in mitochondrial biology would best fit this position. Required Qualifications: PhD in cell biology, molecular biology, stem cell biology, developmental biology, immunology, or cancer
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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
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of behavior. Required Qualifications: a PhD (must be conferred before appointment start date) research experience in a related field at least one peer reviewed scientific publication able to collaborate in
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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
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conjunction with the Basic Science and Engineering (BASE) Initiative of the Children's Heart Center at Stanford University and the Department of Genetics to work on understanding mechanisms of pulmonary
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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
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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
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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