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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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Learning Algorithm for Grid Optimization linked to Bayesian uncertainty outputs Test bidirectional interaction: Bayesian updates → Reinforcement Learning policy adaptation → grid performance feedback
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are developing algorithms and tools to address these bioimage analysis problems, which are all driven by real biomedical research. Depending on the background and interest, the student will have the opportunity
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system requires effective orchestration that can schedule the application on these systems. While traditional scheduling algorithms exist, these do not focus on the energy footprint of applications
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concise code Diligence in implementing, testing and documenting algorithms and results Curiosity about a deeper understanding of Deep Learning architectures What you can expect Fascinating challenges in a
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, forecasting, adaptive learning algorithms, and long memory, both from theoretical and applied perspectives. Empirical applications concentrate on macroeconomic, financial and climate data. Your mission
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, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a sustainable
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promising alternative to classic methods of process optimization due to its adaptive decision making and data efficiency. In this thesis, a BO algorithm for the optimization of laser processes is to be
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environment. We are creative, we shape technology, we design products, we improve processes, we open up new paths. Our clients value our modelling competence, algorithms and software products. We
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, but also in traffic monitoring or in the media context, for example when it comes to automatic metadata extraction and audio manipulation detection. Another focus is the development of algorithms