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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
. Position Summary This Postdoctoral Research Associate will develop novel optical, electrochemical, colorimetric, lateral flow and wearable sensors for various pathogens and analytes. The Research Associate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
. Position Summary This Postdoctoral Research Associate will develop novel optical, electrochemical, colorimetric, lateral flow and wearable sensors for various pathogens and analytes. The Research Associate
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and assessing weld quality of mechanical parts in real-time by developing machine learning models that use sensor data and other tasks that are assigned to you. Core Responsibilities: Understanding
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the holistic understanding of space and its broader societal, ethical, and environmental impacts. Through responsible, multi-disciplinary research, including novel sensors, environment modelling
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quantitative genetics and maize breeding. -Utilizes skills and knowledge in these and other areas to complete research projects leveraging new data extraction and analysis algorithms and connecting
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aims to develop algorithms for discovering patterns that are robust to data changes. They are expected to co-develop the problem definitions, design reslience measures, co-developing and implementing
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of algorithms and research tools. Applications will be reviewed starting September 1, 2026, and will continue to be considered until the positions are filled. Further information and application instructions
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at King’s on quantum algorithms and numerical methods for many-body dynamics beyond the reach of standard classical simulation. About the role We have a high preference for a candidates who is willing
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for Advanced Mathematics for Energy Research Applications (CAMERA) has a new opening for a postdoctoral scholar to develop cutting-edge mathematics and algorithms to analyze complex data from Department
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analyses) using data obtained from several sources. The aim is to expand the theoretical framework for predictive asset management in the railway sector including establishment of suitable algorithms and