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analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET). The candidate will contribute to the design, development, and
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decisions focusing mainly on interpretable machine learning and its applications. The candidate must be an expert in music generation and Schenkerian analysis. The candidate will be responsible for working
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-funded Duke lab hub for the Human Health Exposure Analysis Resource ) program, the Newborn Epigenetics STudy (NEST) longitudinal birth cohort, the Children’s Health and Discovery Initiative (CHDI
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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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, surveillance, control, survivorship, quality of life, health disparities and social determinants of health. Our projects are both US-based and international, and given our transdisciplinary focus, our projects
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approaches to understand the interconnections between nutrition, health, food systems, and society. Our lab has pioneered genomics techniques that objectively track dietary intake across hundreds of plant and
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for diverse social and homeostatic states. The lab uses a combination of circuit-based strategies and custom engineered designs to investigate how social and non-social salient information is encoded in neural
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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Bioinformatics expertise required for scRNAseq analysis. · Previous cell culture experience. · Perform molecular, cellular, biochemical and immunological analyses. · Optimize and troubleshoot experimental
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will include: 1. Implementing and quality-assuring MR Fingerprinting scans on multiple clinical scanners 2. Integrating advanced imaging tools for image processing and analysis. 3. Coordinating closely