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Field
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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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learning arenas. Symbiosis aims to reinforce the foundations for responsible, trustworthy, and sustainable use of AI in our educational institutions by developing ethical and sustainable principles to guide
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assessment, you will develop new, sample-efficient optimal control approaches for gate calibration and test them in numerical simulations. You will pursue your research with the German research collaboration
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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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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to create secure, autonomous and developable solutions that interact with each other and their surroundings, from the edge to the cloud. Project description For this position, you will be working as part of a
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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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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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involves the use of quantum chemistry, machine learning, and genetic algorithms to search for new homogeneous chemical catalysts. Who are we looking for? We are looking for candidates within the field
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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