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under the FIRST TEAM FENG project, No. FENG.02.02-IP.05-0039/23, entitled: „GenProSyn – an innovative generative approach to enumeration of easy-to-synthesize ligands with optimized properties”. Project
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athletics or professional sports organizations. Familiarity with ticketing systems, CRM tools, and revenue optimization models. Experience conducting statistical evaluations for team or player performance
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farms. This research will build upon a multi-scale and multi-physics numerical modeling framework, with the goal of enabling rapid and robust probabilistic assessments for design, optimization, and
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research, optimization, network programming and routing Optical metrology for surface texturing and imaging Remote sensing and photogrammetry Drone mapping Surveying with total station LiDAR and Optical
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NAnoparticles by combining experimental and numerical approaches ), funded in 2024 by ANR – The French National Research Agency. Nanoparticle (NP) synthesis is a viable way of pursuing the production of next
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 24 days ago
opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 9 days ago
opportunities for career growth, skill development and lifelong learning and enjoy exclusive perks that include numerous retail and restaurant discounts, savings on local child care centers and special rates
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on microfluidic and physical principles, fabricate & optimize using state-of-the-art microfabrication techniques and characterize chip performance (fluidics, mechanics, and reproducibility). Method development
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method and to optimize this method for realistic high-performance computer calculations. Subsequently, this newly implemented method will be used in combination with embedding techniques to describe
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training pipelines. Background in numerical analysis and/or optimization, with interest in error estimation, convergence, and robustness to noise. Experience with geospatial data (LiDAR/photogrammetry/GIS