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, especially in ultracold quantum gases or condensed matter theory Proven analytical, computational, and modelling skills Experience with numerical simulations of quantum or many-body systems A deep curiosity
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-computer interfaces, cognitive rehabilitation, and neural prosthetics. Your contributions will support the development of a custom CMOS-based SNN processor that can operate in ultra-low-power environments
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/ Robust) Combinatorial Optimization, Game Theory, and Network Theory, as well as Artificial Intelligence. Potentially, scenarios could be simulated using agent-based, discrete-event, or other techniques
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. The department focuses on the production, analysis, and simulation of functionally optimized structures from the nanometer to the micrometer scale in electrochemically active materials, as well as the development
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appointment) in digital text analysis, digital humanities, linguistics and literature, applied linguistics, communication sciences, computer sciences or similar. You have a background and/or strong interest in
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high-quality research on interpretable and learning-based stochastic optimal control for over-actuated electric vehicles, with a focus on ensuring robustness and fail-safe operation. You will: - Develop
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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and handling are preferred Planning and conducting experiments related to fire safety, combustion, or other thermochemical processes Experience with CFD simulation of fire scenarios, combustion
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employed to translate spatial gene expression profiles from healthy tissues to disease states, and vice versa. This capability will allow researchers to simulate the effects of drug treatments on spatially
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learning in simulated and indoor/outdoor environment. Reasonable results can be achieved in high signal-to-noise ratio environments; further research is required to improve deep learning in fast variation