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), to work on problems at the intersection of biology, medicine, mathematics and computation. The successful candidate will contribute to the development of next-generation learning algorithms to understand
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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quantitative AI algorithms and their applications to PET, MRI, EEG, behavioral, clinical, genetic, and proteomic data. Prepares documentation of existing and newly developed brain image analysis pipelines
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verification algorithms and prototypes on large microgrids, Naval systems, and utility systems. ● Assist grant proposal writing, work collaboratively with industry and government, and mentor graduate
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, behavioral, clinical, genetic, and proteomic data. Prepares documentation of existing and newly developed brain image analysis pipelines, algorithms, and quantitative methods. Assists the team leader in
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verification algorithms and prototypes on large microgrids, Naval systems, and utility systems. ● Assist grant proposal writing, work collaboratively with industry and government, and mentor graduate
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communication theory, signal processing, and wireless communications, • Extensive experience in physical (PHY) layer algorithm design and performance analysis, • Proven track record
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
and generative models. Develop novel algorithms for generative modeling tasks and optimize LLM/GPT-like models on large datasets. Stay abreast of advancements in language modeling and generative AI
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faculty, and conducts a wide range of collaborative quantum research in the areas of quantum computing, quantum algorithms and complexity, quantum cryptography, quantum program verification, quantum machine
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algorithms for inferring multi-modal, condition-dependent networks from datasets with millions of samples (cells) between tens of thousands of nodes (genes and genetic features). Design robust evaluation