82 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at Stanford University
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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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large-language model applications in healthcare systems, systematically identifying ineffective clinical processes, bioinformatics analyses of population health, as well as more conventional outcomes
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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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, statistical modeling, and visualization from a recent project. Contact information for three references – at least one reference should be a past supervisor and another a past team member. Stanford is an equal
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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
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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