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and cell differentiation. We develop technologies to generate statistical data sets that make these problems tractable, as well as computational tools and laws governing biological system behavior. We
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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on international relations, international security, and related issues in the social sciences. The Predoctoral Fellow will be part of the newly established Program on International Relations and International
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psychometric testing; leading and contributing to peer-reviewed journal publications; publishing and presenting findings to a wide variety of audiences using different methods; activities to translate research
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enthusiastic Research Fellow to help in the development and application of novel 3D bioprinting methods for disease modeling and therapeutic tissues. The ideal candidate will report to the Principal Investigator
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scientists whose research advances the fundamental understanding of intelligence. The expectation is that the focus of the fellow’s research will be computational, and not bench-based. Fellows serve for a term
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our lab. Potential applications of interest include artificial extracellular matrices for regenerative medicine, breadboards for localized molecular computing, and nanophotonic devices. Present
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interpersonal and communication skills. While not a must, a strong background in computational methods and/or statistical methods is a plus. Special Instructions Applicants should submit a formal application and
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imaging and microscopy methods, and perform computational data analysis. Previous experience in translational cancer biology or immunology, spatial biology, and microscopy are preferred but not required
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these methods to address challenges in scientific discovery and precision medicine. We seek highly-motivated applicants with background in one or more of the following areas: agentic AI, geometric deep learning