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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
parallel experimental streams Experience with host-pathogen interactions, immune regulation, innate and adaptive immune responses Special Physical/Mental Requirements Special Instructions For information
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redox balance. Our studies explore how p53 integrates metabolic cues by acting as both a sensor and regulator of cellular metabolism. In parallel, we are identifying metabolic changes that promote tumor
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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neurons using multi-omics, cis-regulatory variants will be mapped onto these enhancers and then their function determined by Massively Parallel Reporter Assays (MPRA). The research will involve stem cell
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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organic analysis as a primary means of detecting life. To optimize the science yield of such missions, it is critical to understand processes that govern maturation of organic matter prior to sampling and
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system. We have a variety of ongoing projects that leverage high throughput reporter assays (e.g., Massively Parallel Reporter Assays) to understand gene regulation in the living brain. We apply
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curious to deliver work that matters, your journey starts here! The Department of Electrical and Computer Engineering (ECE) ranks among the best in the country. Our research programs are at the forefront
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). Expertise in data and model parallelisms for distributed training on large GPU-based machines is essential. Candidates with experience using diffusion-based or other generative AI methods as