82 phd-mathematical-modelling-population-modelling Postdoctoral positions at Stanford University
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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applications for a postdoctoral fellowship position to join a project investigating trafficking risks in charcoal supply chains in Brazil. The position is open to recent graduates of PhD programs in statistics
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be made better. This is a role for a researcher excited to work with big, messy, real-world data, motivated not just by building models but by improving systems: how clinicians work together, how
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, with a focus on combination therapies that account for cell-cell interactions. Experimental model systems will include cancer cell lines, organoid/assembloid models, and clinical tumor samples. Projects
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) Epidemiology and Population Health Neurosurgery Postdoc Appointment Term: 2 years (can be extended) Appointment Start Date: July 1, 2025 (Flexible) Group or Departmental Website: http://med.stanford.edu
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Posted on Thu, 03/20/2025 - 22:13 Important Info Deprecated / Faculty Sponsor (Last, First Name): Dirbas, Frederick MD Other Mentor(s) if Applicable: Billy Loo, MD, PhD, Ted Graves, PhD Stanford
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-cigarette nicotine vapor, augment model AAA. Exposure to tobacco smoke, nicotine, and vaping can cause cellular epigenetic alterations, which may be transmitted in a transgenerational fashion. Our data show
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learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high‑performance computing cluster offer an unmatched playground
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. Develop and apply ab initio computations, molecular dynamics simulations, and machine learning models. Collaborate with other researchers within the group and external partners. Present research findings
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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