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Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks (PINNs), Biologically Informed Neural Networks (BINNs), and
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, and (b) the critical role of structural and functional connectivity using combined tractography and graph theory analyses. Our modeling of mnemonic representations uses the latest tools available to AI
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expected to engage in research at the interface of mathematics, data analysis, and life sciences. Possible research topics include mathematical modeling of microtubule dynamics in neuronal cells, analysis
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Activities: Research Topic: Bioengineered Human Tissue Model for Juvenile Dermatomyositis Job Description: Our lab is focused on using bioengineered human muscle systems (myobundles) to research rare pediatric
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will lead methodological innovation and applied research in predictive modeling for mental health, drawing on diverse data modalities such as: Electronic Health Records (EHR) Patient-reported outcomes
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cultured cells and animal models of skin diseases. Work Performed • Development of new and implementation and modification of existing experimental procedures. • Data preparation for oral presentations
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, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control the dynamics of microbial communities in time and space. Ongoing projects
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-time academic or research career. The individual will work primarily on the Duke Predictive Model of Adolescent Mental Health (Duke-PMA) study, a multi-site NIH-funded project that leverages artificial
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interact regularly with the PIs to design and execute experimental studies involving animal and cell-based models of metabolic disease. In addition, you will also perform the following activities: Research
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MD or PhD or equivalent degree and has interests in immunotherapy and/or hematopoietic stem cell transplantation using mouse animal models. The research involves understanding the mechanisms underlying