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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational
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experiments or existing data. In this research, the Post Doctoral Scholar may employ recent interpretability methods such as causal interventions, dictionary learning and circuit analysis, but other kinds
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that you are eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link
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“Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 hours ago
Education & Experience: PhD in Computer Science, Visualization, Cartography, Geographic Information Science, or a closely related field. Strong background in visualization, computer graphics, or perceptual
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their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7 master
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imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely connected
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and acquire spectral data Perform Raman spectral data analysis using machine learning algorithms Work with DiSTAP plant biology collaborators, training researchers in spectrosocpy Work with local
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-centered learning as well as how students from backgrounds traditionally considered “outside” the academy experience and engage in STEM learning and career training. The postdoctoral associate will
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Postdoctoral Associate to begin July 1, 2026. The one-year term position is renewable for an additional year based on performance and is part of Cornell’s Active Learning Initiative . This initiative supports