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Field
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potentially integrated route to generate coherent THz radiation, while offering clear knobs—optical power per line, coherence, stability, and spectral bandwidth—to optimize the resulting THz performance
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, specifically methods that combine machine learning and optimization with physics-based simulation and/or physical constraints and translate these methods into impactful industrial applications. The position is
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, the Optimized AI team focuses on computing techniques to scale up a wide range of AI approaches and drive the SW-HW-Technology co-optimization. We analyze and implement AI models and software architectures while
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therefore a key challenge for the green and digital transition. In this project, you will apply state-of-the-art AI methods to optimize functionalized membranes for selective rare-earth recovery, helping
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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to pose increasing risks to public health in Europe. This project aims to develop a new data-driven framework to assess invasion risk and optimize mitigation and surveillance strategies in the EU. The
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optimize production. Supervisor: doc. Ing. Martin Kotus, PhD. More information about PhD position: https://tf.uniag.sk/sk/doktorandske-studium-2 Additional admin information: renata.zahorska@uniag.sk Where
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Palmas de Gran Canaria (ULPGC) is seeking a Doctoral Researcher (ICP1 level) to join the research project: “Optimal Design of Multipolar Stimulation Patterns in Cochlear Implants through Numerical
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scales, and require high-dimensional parameterizations. A single high-fidelity simulation of the full system is often too expensive to be used repeatedly, for instance in optimization, uncertainty
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environment. Key research Objectives: AI Innovation (Taxonomic Identification): Developing and optimizing deep-learning architectures (e.g., YOLO) for the automated detection and classification of nocturnal