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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 behavioral measures. · Collect, process, and analyze multimodal datasets, including neural, physiological, and motion-tracking signals. · Develop and refine research protocols and methodologies in
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protocols and equipment. · Design, execute and document experiments with sufficient detail to understand and repeat by other lab members. · Update lab members on new developments in commercial reagents and
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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among
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mapping the critical gene and cellular networks by which the Xist lncRNA ribonucleoprotein particles (RNPs) promote autoimmune diseases (see Dou et al., Cell 2024 , for more background). This work will
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polyfluoroalkyl substances (PFAS) in North Carolina, the International Network For Researching, Advancing, and Assessing Materials for Environmental Sustainability (INFRAMES), and more. Our location in
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microbial community behaviors. We seek to understand and engineer the spatiotemporal behaviors of biological networks using tools from systems and synthetic biology. A major goal is to design novel strategies
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implementation of citizen science initiatives, including outreach programs, sample collection protocols, IRB compliance, participant recruitment, and data return strategies Collaboration with international
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national networking opportunities within Duke and in the broader scientific community. This is a full time, grant funded position, eligible for all Duke benefits (https://hr.duke.edu/benefits ) with
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related field prior to the start date. Responsibilities for both positions will include collaborating on the development of research protocols, data collection and analysis, budget and supply management