32 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Postdoctoral positions at Aalborg University
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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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, 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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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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stronginterest and experience with GIS data and tools for urban mobility with someprogrammingskills of Python/R, JavaScript, database management environments, Geographical AI and machine learning workflows
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working with medium and low voltage high power power modules power converters and power magnetics interfacing to ac sources as electric machines and other power converters. It is also critical relevant you
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advanced laboratory and workshop facilities, and a collaborative environment that supports innovation, knowledge sharing, and professional development. Learn more about AAU Energy at www.energy.aau.dk . How
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sharing, and professional development. Learn more about AAU Energy at www.energy.aau.dk . How to apply Your application must include the following: Application, stating reasons for applying, qualifications
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competencies The ideal applicant profile should demonstrate expertise in one or more of the following areas: optimization algorithms (heuristics, metaheuristics, exact methods), learning-based approaches