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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours 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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data pipelines Comply with data security guidelines Data analysis (25%) Data reduction of predictor and outcome measures Determine and implement optimal strategies for testing multiple hypotheses Utilize
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
meteorology, and numerical modeling techniques with an understanding of local-scale physical and chemical processes from multiple emissions sources as represented in current models, and quantitative analysis
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development as well as field data collection at multiple test sites in Ethiopia. Job assignments The successful candidate will join a large, collaborative team of researchers with expertise in electromagnetic
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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vision for higher education, research, and public service. Purpose The Postdoctoral Researcher will develop algorithms for speech anonymization in real time. The applicant should have strong background in
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data, metabolomics and/or proteomics. Develops robust pipelines for data annotation, analysis, and quality control. Creates analytical algorithms and tools to address scientific questions with big data
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control protocols, and hybrid quantum-classical algorithms. o Modeling and benchmarking atomic qubit architectures. · Collaboration: Work closely with experimentalists to design and optimize protocols