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background in microfabrication. Further, experience with experimental setups, large-scale data acquisition and analysis, FPGA programming skills, and knowledge of Python and C++ are important assets
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learning (RL), such as (but not limited to) Theory of online learning, reinforcement learning, and data-driven control Learning in games, and multi-agent RL RLHF and alignment in LLMs Representation learning
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decline at a large scale, linking these metrics with forest composition. This role involves employing data fusion methods, integrating forest structural characteristics, climatic, and topographic variables
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integrating quantum control and quantum optics techniques with neutral cold atoms and their highly-excited Rydberg states, we aim to engineer large-scale quantum many-body systems and pioneer innovative schemes