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on quantitative methods such as applied simulation, big data, artificial intelligence, and machine learning as well as an ability to apply such skills to interventions research, field experiments, and
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information and previous trajectory history. The VP AURI (Active Uncertainty-aware Robot Imitation) project aims to apply Bayesian machine learning methods, such as Gaussian Process Regression and Bayesian
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machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital co-simulation
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corrosive stimuli/loads thus laying a knowledge-based foundation for design of AM-defect tolerant microstructures. The work will be based on experimental characterization guided by advanced AM simulations
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capability to model complicated magnetic systems for two reasons. First, all magnetic sources in a simulation interact, leading to computational resources scaling with the number of sources squared – which is
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info for two academic references Copy of MSc diploma* Copy of Master thesis (in any language)* List of exams with ECTS and grades Recommendation letter (1 page) *If you haven’t completed your Master
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co-simulation platforms (e.g., TRNSYS-Python) Implement and test AI-enabled smart energy management strategies in real-world settings Conducting in situ measurements, including planning, setting up
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image quality in time-resolved micro and nano CT data. Develop and apply numerical implementations in simulation studies as well as to large experimental micro and nano CT data. Explore multi-modality
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. education, experience, language skills and other skills relevant for the position 3. Certified copy of original Master of Science diploma and transcript of records in the original language, including
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tolerant microstructures. The work will be based on experimental characterization guided by advanced AM simulations. The latter being the responsibility of other participants of the MicroAM project