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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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software frameworks, algorithms, robust testing and validation methods, and/or empirically validated solutions that contribute directly to social good, promoting trust, fairness, transparency, and
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build the sustainable companies and societies of the future. The EISLAB division of the Department of Computer Science, Electrical and Space Engineering conducts research within Cyber-Physical Systems
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however require a significant measurement effort to be estimated, especially in the high-precision regime relevant for fault-tolerant quantum computing. You will investigate recent advances in
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, Helmut Schmidt University, and Hamburg University of Applied Sciences Teaching language English Languages All relevant courses are held in English. Programme duration 6 semesters Beginning Only
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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information from high-quality videos that share content with distorted footage as constraints in the learning process of modelling algorithms. This method uses the characteristics and knowledge embedded in high
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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and reproducible research, e.g., in the development of codes and algorithms. We will focus on devising computational solutions that can immediately be of use in other applications contexts as well
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the development of new algorithms for processing, analysis and inversion of active and passive seismic data and the application of these algorithms to field data. Student type Future Students Faculties and centres