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cover all aspects of the drug discovery process. The Postdoctoral Research Associate will receive professional development training, gain valuable networking experience, and collaborate with scientists in
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. The postdoctoral fellow will lead the development of cutting-edge, explainable graph neural network (GNN) models that integrate microbiome functional profiles, host genetic variation, and behavioral phenotypes from
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directly from raw signals. The work spans algorithm development, advances in model-based AI, and translation to real-world clinical applications, offering an opportunity to contribute to next-generation
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cylindrical cells. The postdoc will analyze chemo-mechanical phenomena in porous electrodes and individual particles, and contribute to the development of physics-based models, including porous electrode theory
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on the design and development of mathematical, probabilistic, and statistical frameworks for drawing inferences from complex biological data in collaboration with scientists at the Snow Centre for Immune
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proposal development and preparation of high-quality publications in top computer security, privacy, embedded systems, sensing, and networking venues. ● Pursue research topics such as protecting
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translational mechanisms in muscle biology and inflammatory disorders. Model Development: Contribute to the design and characterization of disease-relevant murine models, including biochemical and pharmacological
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writing research proposals and publications Participating in academic mentorship of graduate students Presenting and disseminating research findings at professional conferences Accessing career development
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development. Our program offers Postdocs job market support and Postdocs may engage actively in department-wide committees, workshops, reading groups, and other pedagogical training offerings. Postdocs also
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interested in uncovering the role of mechanics in tissue development, healing, and homeostasis. The postdoctoral research associate will perform basic or applied research of a limited scope, primarily using