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Field
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization
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the case of challenging environmental conditions, the method of multi-scale parallel single-pixel imaging has the potential to enable breakthrough advances. The “image processing and AI” group contributes
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the autonomy of each member, supports individual capacities for growth and tolerates differences in opinion; treat all faculty, staff and students as individuals, hold them to equal standards and provide
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dedicated to an ethic of civil and free inquiry, which respects the autonomy of each member, supports individual capacities for growth and tolerates differences in opinion; treat all faculty, staff and
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ecotypes and seek to identify their associated and underlying genetics. Specifically, it will examine parallel evolution of genomic divergence, gene expression levels, and alternative splice variants
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Description Electromagnetic techniques can provide a means for rapidly analyzing or processing biochemical samples in a manner that can be readily scaled up to handle large numbers of samples in massively
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dedicated to improving quality of life across the world. This position will play a crucial role in ensuring the continuous operation and enhancement of our High-Performance Computing infrastructure
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systems based on massively parallel hardware architectures Combination of programmable logic, tensor processors and general-purpose CPUs for real-time adaption and scheduling services (e.g., AMD Versal