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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning
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for experiential learning on and off campus, including a growing emphasis on international experiences through short- and long-term study abroad. Students also have access to a robust program of applied research and
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. Connections working at Princeton University More Jobs from This Employer https://main.hercjobs.org/jobs/22428120/postdoctoral-research-associate-ai-machine-learning-for-biochemistry Return to Search Results
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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analysis, computer vision, or machine learning, with a clear interest in developing image analysis algorithms and an affinity with medical topics. Good communication and organizational skills are essential
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challenge our students, staff and faculty to maintain such standards and we are delighted to have those join us, who can help us in our pursuits. Learn more about us by visiting our website at https
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machine learning and data harmonization approaches for water quality monitoring and assessment. Responsibilities include developing and validating computational methods to standardize and infer nutrient and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
of microscopy, multiplexed immunofluorescence, spatial/protein imaging, or image-derived single-cell phenotypes. Machine learning, latent variable modeling, variational autoencoders, optimal transport, graph