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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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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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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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interest to analyze genomics data and interpret biomedicine from data; experience or interest in cancer genomics, tumor microenvironment, single-cell data, spatial omics, immunology, gene regulatory networks
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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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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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. This support includes access to a Titan Krios and Tundra TEMs, fast network interconnects, all-flash network storage, high core density CPU servers, and AI-optimized GPUs. The position is for 2-4 years depending
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(e.g., Planet-scope; NASA’s EMIT, GEDI, and ECOSTRESS; NEON’s AOP products) alongside diverse ecological data (e.g., monitoring network, PhenoCam, Citizen Science data). Responsibilities include