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Computational Arts, Music, and Games within the DSAI division. About the research project This position is related to investigating learned cultural representations in data search spaces of generative AI models
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testing dynamic equivalencing methods for power system dynamic simulations and integrating these into commercial simulation tools. Dynamic equivalents are simplified representations of complex power system
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, and heat within a robust energy system Develop methodological approaches and assumptions for a realistic representation of uncertainties and disruptions in energy system models Evaluate scenarios and
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and custom processors. Work description Investigate SSM representations compatible with spiking dynamics Investigate the usage of SSM along with sensor devices. Design digital/analog building blocks
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behavior of programs at a high level. Automata theory — to manipulate logical formulas and domain representations. Two-player games — to reason about strategies and synthesized programs. The work involves
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support the development of a European energy system model by benchmarking future technologies and optimizing their representation within the FINE optimization modelling framework ( https://github.com/FZJ
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the collaboration. In particular, the following open questions will be addressed: What is a sequence-structure-function representation space for molecules in the context of closed-loop optimization, that supports
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of natural and technical options for provision of negative emissions · Simplified representation of CO2 transport and CO2 storage in the energy system model · Development of scenarios for the use of CO2
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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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transportation decision-making by exploring a wide range of possible scenarios, including unexpected and negative outcomes. By integrating machine learning, metamodeling, and causal representation learning, we aim