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to augment classical spike train analysis methods particularly those developed by Prof. Grün and others for detecting synchronous spiking activity with AI-based enhancements. After profiling the classical
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manipulation in microfluidic environments Design and implement reinforcement learning algorithms for control and manipulation, first in simulation and later on real experimental setups Refine a real-time
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others for detecting synchronous spiking activity with AI-based enhancements. After profiling the classical methods for their bottlenecks, these steps will then be replaced or supplemented with ML-based
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manipulation, first in simulation and later on real experimental setups Refine a real-time planning and execution architecture for information-driven experiment steering (closed-loop control) Work in an
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. After the first year of study, your academic achievements will be assessed. If this shows that you will successfully complete your programme within a reasonable period of time, the scholarship will
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years as the desired scholarship duration in your application. After the first year of study, your academic achievements will be assessed in a further funding procedure. If this shows that you will