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or related techniques, and/or (3) computational biology are encouraged to apply. Lab overview: The communication between cells and their environment depends on decoding of extracellular cues into intracellular
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. Environment and Resources The Kalbasi Lab is housed in the Department of Radiation Oncology in the Stanford School of Medicine and Stanford Cancer Institute. The lab is also affiliated with Stanford Center
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precision medicine in a collaborative environment focused on mechanistic discovery. The Sheltzer Lab is located at Stanford University in Palo Alto, California, which provides exceptional resources and
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. The postdoctoral position offers the opportunity to work at the intersection of genetics, developmental biology, and precision medicine in a collaborative environment focused on mechanistic discovery. The Sheltzer
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be tailored to match each postdoc’s strengths and growth goals. If you are passionate about building high-quality neuroscience experiments and data systems in a highly interdisciplinary environment, we
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Invasive and Bariatric Surgery Research Lab in the Department of Surgery at Stanford University School of Medicine. Our lab is distinguished by its collaborative environment, allowing the researcher to work
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/ (link is external) How to Submit Application Materials: Send application materials to environment@stanford.edu (link sends e-mail) . Please use Solar Geoengineering in the subject line. For full
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for Human and Planetary Health, an interdisciplinary Center at the Woods Institute for the Environment that advances solutions to improve health and sustainability. Position Summary: We are seeking a skilled
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of urothelial exfoliation in cancer therapeutics. The labs are committed to fostering a highly collaborative and scientifically rigorous environment. This position offers an excellent opportunity to learn about
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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