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: Software Development Develop, optimize, and maintain software for AI research projects. Collaborate with researchers to implement state-of-the-art machine learning models. Work with large-scale datasets
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, what is the required licensure/certification? Required Computer Applications: Microsoft Excel, Microsoft Word, Microsoft PowerPoint, Microsoft Outlook Required Other Computer Applications: Required
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or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
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of machine learning What We Offer: On the basis of full-time employment (40 hours/week) the minimum salary in accordance with the collective agreement is € 5,014.30 gross per month (14 x per year, CA Job Grade
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expertise in sensor design, experimental testing, modelling, machine learning, and industrial collaboration, positioning you to contribute directly to next-generation inspection technologies for aerospace
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-learning–based segmentation, classification and tracking for microbes and microgels in phase-contrast and fluorescence images Optimise these models and pipelines for real-time performance and integrate them
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contributes $10.6 billion in economic impact to the city and $22.7 billion to the state of Ohio. At UC, next is all of us. Learn more at uc.edu . UC is a mission-driven organization where we are committed
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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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: https://resources.uta.edu/hr/services
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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden