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students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading
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competencies The applicant must hold a master’s degree in engineering and a PhD in a relevant field, such as electrical engineering, with expertise in physics-based modeling, machine learning, and optimization
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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments
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one of the following topics: extended reality (virtual reality, augmented reality) human-computer interaction computer vision The capability to successfully conduct research projects in
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. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and process-based radiative transfer models
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, mechanical and durability testing, and integration with advanced machine learning models. The postdoc will collaborate closely with CEBE’s parallel work packages. Experimental and analytical data generated in
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. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and process-based radiative transfer models
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control venues such as the IEEE Conference on Decision and Control and IEEE Control Systems Letters, and in top machine learning conferences such as NeurIPS, ICML or AAAI, is expected. Proficiency in MATLAB
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project. The research will bridge both established and emerging technical expertise within the section, encompassing areas such as FPGA and neuromorphic computing, Edge AI, machine learning, power
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learning environments. The successful applicant will contribute to both the theoretical development and the empirical implementation of the project, working closely with schools, teachers, children and