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Field
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for autonomous drone systems Multi-agent cooperation using Reinforcement Learning and control barrier functions Mission planning for aerospace systems using evolutionary algorithms and Reinforcement Learning
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computing to develop a continuous and local alternative to existing gradient-based learning rules, bridging theories of predictive coding with event-based control/ Simulate models of the learning algorithm
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity
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computing, domain-specific multi- and manycore architectures, networks-on-chip (NoCs), methods and algorithms for application parallelization, simulators and virtual platforms for application- and
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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letter, CV, a list of your most important publications with an explanation of your own contribution, and a list of development projects you have completed. Please also provide information on your earliest
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. The project’s overarching goal is the development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire
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including your academic CV, list of publications, a research concept (3-5 pages) and, if applicable, a list of previous third-party funding and teaching activities to Rositta Mothes: rositta.mothes@physik.uni
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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
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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