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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
, or other novel/emerging pollutants - Developing / implementing advance machine learning algorithms for environmental datasets - Attention to detail and careful documentation of work products such as How
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are: i) to develop a new selected CI algorithm allowing reaching chemically-accurate results for large compounds; ii) to extend the currently-available database to properties relevant for both core
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sustainability. The selected researcher will contribute to the development of predictive models and machine learning algorithms for data analysis from plant-based sensors, multispectral and thermal imagery, and
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operations. The PhD will develop and implement artificial intelligence and data-driven methods for early anomaly detection, root cause diagnosis, and failure prediction on industrial systems, leveraging
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The opportunity The University of Liverpool is a key partner in a £14 million initiative (https://tinyurl.com/yc5z768m) to develop a sustainable, next-generation manufacturing facility, using
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-precision localization and radar-based sensing. You will develop adaptive algorithms that dynamically select the optimal radio configuration based on the specific requirements of each application. The radio
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your tasks: Developing optimization algorithms for massively parallel hardware architectures such as AI
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responsibilities will span all stages of research, including collecting data of in both tabular and spatial formats, developing algorithms that clean and organize data, conducting statistical analyses, running
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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situation in which a customer or colleague was upset and the steps you took to resolve the issue to a reasonable conclusion. Describe your key impressions of the CVC presentation found here: http