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the communication system for prototypes of the autonomous monitoring system for invasive wasp species (Asian hornet); (2) to support the programming of AI algorithms for image recognition; (3) to develop the web
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
/software engineering support, leading the development of and contributing to semantic pipelines and algorithms, data models, ontologies, and semantic data harmonization and integration strategies for a
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description languages. Develop and optimize EDA workflows for processor and accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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, and visualization techniques to assist with the development, evaluation, and/or applications of algorithms, methods, and software for data analysis. Responsibilities include querying databases, data
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candidates will have specialist knowledge in signal processing and algorithm design, with experience in machine learning, AI system development and reinforcement learning along with a strong publication record
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations