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candidate with a strong background geology/geomorphology, or a related discipline, a strong interest for evolutionary biology, and who is interested in bridging field data, computational modeling, and large
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setups, large-scale data acquisition and analysis, FPGA programming skills, and knowledge of Python and C++ are important assets. The project is highly interdisciplinary (microtechnology/electronics
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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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