81 phd-computational-"IMPRS-ML"-"IMPRS-ML" Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
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
-
competitive funding if interested. Maintain meticulous research records and support operational/reporting responsibilities associated with clinical research. Required Qualifications: A PhD in computational
-
Program at the Stanford Cancer Institute. She has an academic interest in Precision Medicine and her lab applies cutting-edge sequencing and imaging technologies to better understand skin cancer and rare
-
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
-
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
-
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
-
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
-
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
-
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
-
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