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supporting multiple sensors (LiDAR, radar, camera) for dynamic environments like ports, airports, and logistics hubs. Optimize algorithms for real-time operation on edge devices with limited computational
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. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation. Previous Work https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10530091 https
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Researchers: Ph.D. in Computer Science or Mathematics, ideally with a background in one or more of the following areas: Optimization, Game Theory, Machine Learning Applicants must demonstrate: • An excellent
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical energy management systems (EMS) controlling complex energy systems like buildings, electricity
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), and joint optimization—impact the model's accuracy, robustness, and generalization capabilities? Can fine-tuning a pretrained model like CLIP on domain-specific data (e.g., automotive or travel-related
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purely motor-powered ships and will be marketable as an independent product. The new system to be developed will be tested and optimized with an existing CargoKite ship prototype in real operation
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main focus on the development of control software. ▪ You will design and implement advanced control and readout protocols and optimize experimental characterization workflow,s leveraging machine learning
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processing - Study actin-based processes critical for health and disease using the cryo-ET workflow, from cell sample preparation and optimization to imaging and data analysis - Use computational tools for in
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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods