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professor, one assistant professor, one postdoc, and a large number of PhD students. The project includes strong partnerships with the University of Leipzig (Bioinformatics, Prof. Peter Stadler
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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with society. Whether our contributions come in the form of excellent research, innovative solutions, education or learning, we must make a positive difference to society and contribute to a sustainable
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML
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some background in one or more of the following areas: Mathematical Optimization / Operations Research Reinforcement Learning, Machine Learning, and/or Multi-agent systems Game Theory Algorithms
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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types of dissemination related to your PhD project Teach and disseminate your knowledge Write a PhD thesis on the grounds of your project Key criteria for the assessment of applicants The successful
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undermine this future. Can you see how Machine Learning, Computer Vision, and Robotics can open up opportunities for autonomously operating agricultural robots? Are you passionate about making agriculture
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models, and reinforcement learning (RL), which is data-driven, are two powerful control techniques. MPC techniques are well-established, while RL techniques are gaining popularity due to increasingly
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is