233 data "https:" "https:" "https:" "https:" "UNIVERSITY OF RIJEKA" Postdoctoral positions in Denmark
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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, high-throughput screening, and state-of-the-art carbohydrate analysis, to validate computational designs and generate data that advances our knowledge of enzymatic carbohydrate synthesis towards
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requirements using pilot-scale or simulation data Publish high-impact research results and present findings at international conferences Contribute to proposal writing and joint projects with academic and
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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description We invite applications for a 2-year postdoctoral position with the possibility of extension. The successful candidate will lead experimental campaigns and data analysis to quantify greenhouse gas
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cross-European research project FOOD-FRAMES. The FOOD-FRAMES project explores how the food information environment across nine European countries affects sustainable food choices, often hindering
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publications in leading international venues. - Guiding and working with Master and Ph.D. students at ECE and collaborators as needed. More information - see the attached link or email: sshreya AT ece.au.dk
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Lyngby Campus, Denmark, You can read more about career paths at DTU here . Further information Further information may be obtained from Professor Yi Sun ( suyi@dtu.dk ). You can read more about DTU
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algorithms and circuits to enhance imaging quality and speed Creating efficient data acquisition and processing workflows for large datasets of skin nanotexture images Optimizing hardware-software integration
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charging strategies for lithium-ion batteries. The goal is to integrate model-based (digital twin) and data-driven (AI) methods to design and experimentally validate optimized pulse charging protocols. A