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and Drug Delivery Systems for Enhanced Bone Regeneration” (Reference: PROID2025010017). This project focuses on the optimization of additive manufacturing processes (MEX) and scaffold performance
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 20 days ago
. These simulations are run on highly parallel supercomputers on which both the hardware and the software are optimized for the task at hand. While the computing power of each processing unit is still increasing, the
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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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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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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