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learning, and policy foresight. This position focuses on the development of travel behavior models sensitive to new pricing and tradable credit schemes for future demand management of urban mobility systems
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will have the opportunity to engage in pioneering research, collaborate with a large, dynamic and multidisciplinary team, and advance the field of quantum computing through innovative algorithms and
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approaches, such as topology optimization, with gradient-free methods, such as genetic algorithm, in order to exploit the specific strengths of the two classes of algorithms, and through this reach an ideal
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and communication systems. You will work with real measurement data and participate in both algorithm development and experimental validation. You will collaborate with industrial and academic partners
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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will: Develop and implement model-based and data-driven (AI) optimization algorithms for battery charging Integrate physics-informed models and data-driven tools to design health-aware charging protocols
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Research Focus We are offering a Postdoctoral position in graph machine learning, algorithms, and graph management with particular focus on: Modeling real-world spatio-temporal energy networks Developing
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advanced modelling, data analysis, and algorithmic development. Your tasks will support our core research focus of mathematical and computational approaches to design and implement solution algorithms, with
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environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental sustainability. You will focus on processing
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theory, and is expected to have experience with the practical implementation of control algorithms. Who we are Learning and Decision at AAU, founded in 2020, focuses on developing mathematical methods