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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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: *Back-end Java development *Front-end UI development *Signal processing algorithm development This position will work on all phases of software application development ranging from requirements gathering
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of promising nanoagents. The objective of this doctoral project is to develop an innovative method for sensing and quantifying the hotspot effect in aqueous suspensions of optically absorbing nanoagents, without
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, perform simulation studies, and apply developed methods to empirical datasets. The positions do not involve any lab work. The work includes mathematical modeling, algorithm development, statistical analysis
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. Activities include ultrafast quantum physics, quantum technology with rare earth atoms, quantum states in nanosystems, quantum information theory, quantum spectroscopy, quantum algorithms for optimization
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. Learn more about Sandia at: http://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: Sandia provides systems, science, and technology solutions to meet national
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developing prototype systems and algorithms • Experience with MRI and/or fMRI data (human data preferred) • Experience with high resolution and high field MRI and/or fMRI • Excellent verbal and written
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junior developers and researchers Stay current with the latest developments in Deep Learning frameworks for weather forecasting and climate science. Where to apply Website https://jobs.fbk.eu/Annunci
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, to create a responsible and innovative university to serve as a model for the 21st century. Within ICN, the ChemSenSim group (https://lab.chemsensim.fr/ ) develops interdisciplinary research projects