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Requisition Id 16687 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 days ago
Summary The Division of Pharmacoengineering and Molecular Pharmaceutics (DPMP) is engaged in research and education in drug delivery options to optimize the therapeutic efficacy of pharmacologically and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 days ago
of Pharmacoengineering and Molecular Pharmaceutics (DPMP) is engaged in research and education in drug delivery options to optimize the therapeutic efficacy of pharmacologically and immunologically active agents. It is a
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-resolution microscopy Genome editing (e.g., CRISPR-based approaches) Quantitative and live-cell imaging Functional genomics The project is embedded in an interdisciplinary research environment combining
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At AAU Energy, a position as Postdoc in AI and Deep Learning for Radar-Based Non-Destructive Testing is open for appointment from 01.10.2026 or as soon as possible hereafter. The position is
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of Energy (DOE) experimental facilities. This role involves research and development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high
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or soon hereafter. Your work tasks The aim of the project is to optimize the production of pigments, which can be used as electrolytes in a battery for storing renewable energy. The work will include
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. The position combines bacteriology and mammalian cell culture with dual RNA-seq to characterize bacterial and host transcriptional responses across serotypes relevant to cattle and food safety. The Postdoctoral
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will contribute to the development, optimization, and characterization of cell lines from marine and aquatic species, working toward scalable, process-ready cell lines for cultivated seafood applications
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs