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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise
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-on experience with robotic hardware (e.g., robot arms, tactile sensors) Familiarity with model-based planning approaches, robot force/motion control, and reinforcement learning Proficiency in programming (C
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: atmosphere.ei.tum.de ) Optimization of an urban sensor network configuration for greenhouse gas and air pollutant measurements using mathematical and physical assessments Analysis of ground-based and satellite-based
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, climate and earth system modeling, soil parameter mapping, and multi-sensor segmentation, together with our partners at renowned international and national institutes such as Bonn University, Alfred Wegener