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will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine
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in areas related to quantum information theory, quantum error correction, and machine learning. The position will start in fall 2026 and will be for two years, with a possible 1-year extension pending
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opportunity to align with the most relevant academic department in the College of Architecture, Art, and Planning and teach one course per year subject to department needs. The Postdoctoral Associate will be a
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of data scientists, data engineers, software developers and many more, that are focused on bringing data, machine learning and statistical modeling into the products that we build for our clients
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interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine their antibiotic resistance. Your work will
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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of the top private universities in the United States, and the Tandon School of Engineering, located in Brooklyn, NY, is deeply committed to excellence in teaching and learning. Tandon fosters student and
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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work to exciting research in multi-disciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and machine learning skills to bear as you develop new methods
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Type Staff Job Description Major/Essential Duties of Job: 1. Develop machine learning or physical based models for plant water stress quantification. 2. Develop machine learning models for crop mapping