-
years prior to the application deadline. Experience with machine learning for scientific applications. Experience with deep learning frameworks such as PyTorch or TensorFlow. Experience with atomistic
-
leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation
-
infrastructure, mobility demand, and power grid operations. On top of this environment, a deep-learning-based learning will be developed to enable decentralized and coordinated decisions on EV user charging
-
executed. In close collaboration with PhD researchers and project partners from TUM and ETH, you will contribute to the development of novel control and learning methods for aerial manipulators and multi