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out-couplers to interface with external light sources. This architecture will serve as a platform for developing both optical sensors and optical signal processors, leveraging existing integrated
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the adaptation of classical algorithms to advanced computing architectures. (0-15) Experience in multimodal or multiscale data analysis in the context of complex pathologies, especially in neuroscience
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software. Knowledge of explainable AI methodologies. Experience integrating heterogeneous instrumentation under unified control architectures. Track record of publications in high-impact journals in
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condensates are dynamic, membraneless organelles formed through phase separation. Their material properties—fluidity, elasticity, and internal architecture—can shift dramatically in response to subtle
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with next-generation computing architectures. Main Tasks and responsibilities: Perform research related to the fields of: advanced memory concepts; voltage control of magnetism; interplay between surface
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systems and automated characterization from our partners at IREC into the AI platform. Expertise in building FAIR-by-design scientific data architectures, semantically indexing heterogeneous data (DFT/MD
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resilience. Objectives: To advance understanding of how soil management practices may influence root system architecture, resource-use efficiency, and plant resilience under compacted or suboptimal soil
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, quantum compilation techniques, and noise-aware algorithms for Rydberg architectures. Apply quantum optimization to real-world problems such as logistics, scheduling, and portfolio allocation, comparing
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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges
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devices dedicated to implementing next-generation QKD system protocols (high-dimensional systems, stable and robust architectures, etc.). Share this opening! Use the following URL: https://jobs.icfo.eu