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essentials, data structures and/or databases, statistical computing and machine learning. Experience: Experience within an academic environment. Experimental design including basic science experiments
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, computer graphics, and embodied AI, including physics-based simulation, character animation, motion synthesis, and learning-based control for humanoid systems. Key Responsibilities: Conduct independent and
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-on experience in NLP or machine learning, including model training, fine-tuning, and evaluation Familiarity with NLP models and toolkits (e.g., Word2Vec, BERT) and with speech-to-text workflows Proficiency in
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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of Georgia leadership institution and is The Military College of Georgia. More details on the UNG Mission, Values, Vision, and Culture can be found at https://ung.edu/about/mission-vision-values.php
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therapeutics and antimicrobial peptides. The Postdoctoral Researcher will apply computational modeling, structural analysis, and machine learning approaches to support mechanistic studies of cellular signaling
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Work Where You Learn: Build Experience, Grow Skills, and Contribute to Your University Community. This position is available only to enrolled American University students. Most student positions
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systems and control theory, circuit theory, optimization, and machine learning, with the ultimate goal of advancing the mathematical foundations of physics-based learning. Your responsibilities include
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 14 days ago
of interest include machine learning and statistical inference for neural data, multimodal neural data integration and harmonization, computational modeling of cognition and behavior, neuroimaging analytics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
of interest include machine learning and statistical inference for neural data, multimodal neural data integration and harmonization, computational modeling of cognition and behavior, neuroimaging analytics