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, analytical and computational skills. Knowledge of Bayesian machine learning, deep learning and statistical learning. Experience in statistical modelling and data analysis; ideally experience with quantitative
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strain theory and advanced hyper elastic material models incl. anisotropy You preferably have insights into topology optimization and/or finite element analysis and are committed to improving state
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. Qualifications You are excited about experimental science and advanced data analysis, and we expect that you enjoy being part of a team, that you have sense of humor, is a good problem solver and that you can work
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/microbiology Experience with writing and running data analysis scripts in a command-line interface Solid Programming experience with: A high-performance, compiled language like Rust or C++ A scripting language
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chemistry, and data analysis, ensuring a comprehensive, interdisciplinary approach to this exciting field of research. You will be part of the research teams of Associate Professor Lars Behrendt and Professor
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, identification, and length estimation. Additionally, it will focus on creating AI-assisted review software to streamline the analysis of EM data, enhancing efficiency and scalability in fisheries monitoring
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is to create and combine knowledge on relevant atmospheric flow statistics with AWE time-domain analysis and uncertainty quantification, to determine loads statistics and failure probabilities
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-connected services, power systems, renewable energy, electric vehicles, etc. Further, to implement advanced measurements (EIS, ICA, pulse-excitation techniques, embedded sensors, etc.), perform data analysis
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from many different sources into data frames that can be analyzed with biostatistical applications in the statistical software R/Python. Performing data analysis in accordance with time-structures and
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to the highly sophisticated analysis of nanoscale materials in hard and soft matter. Since 2018, DTU Nanolab is extended to provide expertise in soft matter from small molecule complexes to biological cells