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the efficiency. The outcomes will be to validate its efficacy against frequent unstable events, handling contingencies, accommodate protection technologies by tokenizing energy-based control and modeling
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education and knowledge dissemination. Job description We seek PhD students that will contribute to new generations of scalable, model-based tools for cyber-physical systems based on a mathematical sound
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and European companies and international academic partners. The AI4OR group focuses on solving complex problems, which require high-level modelling, data analysis and management capabilities as
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about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its
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limitations of current EMG-based techniques, particularly in dynamic movement tasks. The PhD student will focus on the design, development, and validation of the HD-sEMG/ultrasound system. This includes
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, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within
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will conduct sampling, and apply experimental methods such as metagenomics and metatranscriptomics, linked to soil and emission data to help create predictive models. Within a broader framework, your
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crystallinity. Therefor prior knowledge of state-of-the-art modelling software and molecular dynamics simulations and quantum mechanical calculations to elucidate the reaction mechanism, together with AI based
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for resilient manufacturing systems. This topic will build upon existing theory on modular and reconfigurable manufacturing systems and develop methods and model-based approaches to design and evaluate resilient
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. Development of numerical models for system descriptions. Lab-scale tests of a number of UV-AOPs (eg. VUV, UV/H2O2 and UV/O3) using a collimated beam approach. The effect of water composition on the AOP