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equipment Experimental design, data analysis, writing of scientific articles and presentations at conferences and meetings Qualifications: As our new PhD student, you must have a two-year master's degree (120
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qualifications will be considered in the assessment: Strong background and interest in dynamic modelling and control Skills and experience with time series analysis and formulation of stochastic dynamical models
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analysis of radionuclides in the environment and radioactive waste for assessing environmental risk, characterization of waste and tracing environmental processes. You will work in the laboratory
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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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. 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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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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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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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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, 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