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modeling and animal modeling. We also collaborate with a number of other groups within the Stanford Cardiovascular Institute, Department of Microbiology & Immunology, and Division of Oncology/Stanford Cancer
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-based models as well as patient-derived xenograft models of liver cancer. This position is suitable for a highly motivated self-starter who excels in a dynamic environment offering varied learning
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the Office of Postdoctoral Affairs. The FY25 minimum is $76,383. Our research team is looking for a postdoctoral scholar interested in lymphatic imaging research utilizing a large animal model of lymphatic
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big data and AI at its core. A central goal of the project is to build a foundation model of the visual brain—a “digital twin” that captures neural activity and intelligent behavior at unprecedented
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–immune interactions, leveraging preclinical models and clinical samples from phase 1 trials. Our goal is to bridge molecular insight with clinical translation. Environment and Resources The Kalbasi Lab is
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different sensory experiences (e.g. deafness). Recent work in our lab has focused on: Developing transformer-based models to classify infant and child speech maturity from naturalistic audio recordings across
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isolate the effects of the chromosomal abnormality from other genetic variation (Lee et al., bioRxiv 2025). This project will expand on this work by generating additional patient-derived models, performing
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analytic skills (e.g., multi-level models, SEM, LCA/LPA), experience analyzing nested longitudinal data, experience with grant writing, experience with project management and data collection, a demonstrated
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, molecular biology, and in vivo models. Analyze and interpret data, integrating experimental and computational findings. Utilize bioinformatics tools and techniques to analyze high-throughput sequencing data
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. Candidate will have the opportunity to investigate human Tregs in vitro and in vivo, learning from patient samples and humanized mouse models, implement state-of-the-art technologies such as functional