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-generation DNA/RNA sequencing, genetically engineered cell lines, and preclinical brain tumor models to understand the molecular mechanisms underlying brain tumor biology. Our overarching goal is to use novel
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requires working with large data sets related to parenting and child development across multiple sites in the United States, helping to prepare data and estimate models for a variety of research papers, and
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spans basic science models, including primary tissues and mouse models, as well as translational clinical research. Qualified candidates will have a strong record of training in biomedical research
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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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science. Other application areas of interest include robust parameter estimation and performance bounds under model misspecification, integrated sensing and communications, orthogonal time frequency space
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biology, and mechanisms of drug resistance. Research activities will utilize a combination of in vitro approaches, including cultured cell models, and in vivo experimental systems using animal models
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academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also, perform data analysis utilizing medical
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other Duke faculty, or faculty at other academic institutions. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also
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digital health and multi-agentic coordination. This role requires the development of self-learning artificial intelligence models tailored for dynamic healthcare environments. The candidate designs and
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. Candidates with background knowledge and hands-on experience in mouse models, proteomics, 3Dorganoids,primary cells purification and culture skills are particularly welcome. Minimum Requirements: Ph.D