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deliver a theoretical, algorithmic, and real-time implementation framework for on-the-fly autonomy in crowds. The resulting methods will (i) adapt to unpredictable human interactions that introduce high
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algorithmic solution development. The group focuses particularly on automated decision-making in autonomous cyber-physical systems, combining mathematical optimization, machine learning, and decision theory
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine learning for improving
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-driven algorithms (e.g., neural networks, reinforcement learning) for the creation of surrogate models and the autonomous optimization of high-dimensional design spaces. Experience simulating hybrid
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algorithms and real-world applications. The job The student will be part of the administration team and will support the daily operations of the program. You will work closely with the administrative
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international journals. Contribute to the design, analysis, and validation of new techniques and algorithms for estimation and inference, including theoretical work, simulations, and empirical applications