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We invite applications for a postdoctoral researcher to join the UMLFF project at the University of Luxembourg. The project aims to develop the next generation of uncertainty-aware machine-learning
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optimization modeling. An ideal candidate would use their foundational knowledge of various modeling methods to develop and apply cutting-edge machine learning and data analysis techniques for assessing and
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). The project investigates how machine learning (ML) can be used to enhance the modelling of boundary layers in industrial CFD simulations, where complex geometries and computational constraints limit near-wall
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 18 hours ago
Position Information General Information Position Number POST40 Working Title Post-Doctoral Fellow-Machine Learning in Thermal-fluid Sciences Division Academic Affairs Department College
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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specialised expertise in the Machine Learning for Engineering sub-theme. Candidates from all areas in machine learning are encouraged to apply, with a special focus on the areas of (i) information theory and
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Machine Learning Methods for Enhancing Autonomy of Unmanned Aerial Vehicles in Wildfire Detection and Localisation School of Electrical and Electronic Engineering PhD Research Project Self Funded
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to communicate the accomplished / in-process work to internal and external stakeholders and adapt directions where needed. Where to apply Website https://www.imec-int.com/en/work-at-imec/job-opportunities/machine
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announces an open competition for the position Ph.D. student - Machine learning-based tools for multiparametric enzyme optimisation Workplace: RECETOX, Faculty of Science, Masaryk University in Brno, Czech
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RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and design Location Material