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conduct research on the theoretical foundations of mathematical optimization, as well as its applications to emerging challenges in machine learning and engineering. You will write and submit research
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. Such methods are founded on mathematical models and algorithms from the field of mathematical optimization. The dose planning is based on medical images and a treatment protocol, and it is evaluated by using key
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, optimizing, and deploying AI models on HPC and GPU-based systems. Provide guidance on performance optimization, scaling, and efficient resource utilization. Contribute to architectural and design decisions in
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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distributed computational pipelines and optimizing communication costs. You will also contribute to the integration and testing of the models in real D-MIMO environments, in close collaboration with a PhD
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existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new distributed computational pipelines and optimizing
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communication limitations, adversarial conditions, continual and adaptive learning in dynamic environments. The research will combine tools from distributed optimization, stochastic approximation, information
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in clinically relevant environments. Key work assignments include: Design, fabrication, and optimization of high-performance plasmonic nanostructures and SERS substrates for sensing in complex
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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international conferences or journals, especially publications in the area of Air Traffic Management Knowledge of optimization techniques Knowledge in the area of design and analysis of algorithms Great emphasis