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/environmental). By considering all aspects, optimized designs and best practice are sought to be found. The new technical design and solutions shall be discussed in workshops with industry experts and ranked
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, implementation, validation and assessment of new solutions, optimization techniques and AI/ML models to support edge intelligence and the next generation of mobile communication networks (5G+/6G) with focus
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
community. University employees can choose from a wide range of professional training opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks for numerous retail
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a grant from the Swiss National Science Foundation entitled Machine Learning for Optimized Ab-initio Quantum Transport Simulations (MALOQ). It officially started on January 1st 2026 and will
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for the recruitment of 1 Doctorate Initial level within the scope of project VirtualTextiLab - Development of a virtual laboratory to optimize functional and sustainable textiles in opensource, with number 15328 and
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experimental, pilot-scale, or high-fidelity simulation data into model calibration and validation workflows Design and run numerical simulations of multiphase flow systems and reactors Quantify model uncertainty
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to join our world-leading research program in robotics, remote sensing, and data-driven approaches for sustainable agriculture. PhenoRob’s mission is to transform crop production by optimizing breeding and
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complex materials simulations. These agents will assist with setting up, executing, and optimizing electronic structure workflows, from standard ground-state Density Functional Theory (DFT) calculations
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and phenotyping for sustainable crop production with the vision to transform crop production by optimizing breeding and farming management through developing and deploying new technologies. Within
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practical applications of advanced machine learning techniques. Emphasis will be given to theoretical approaches in machine learning for real-world applications, with a preferred focus on optimization, data