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Field
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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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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- “Reliable industrial foundation models for trustworthy industrial robot fault diagnosis in automotive smart manufacturing”. He/She will be required to: (a) carry out research in advanced machine learning
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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learning, or computational research Required Experience Demonstrated experience building, training, fine-tuning, and evaluating machine-learning or deep-learning models using established frameworks such as
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goals will also be explored during the later stages of the selection process. You should also have: solid knowledge of artificial intelligence and machine learning techniques, including their limitations
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of tropical fruit, vegetable and ornamental crops grown in the Pacific Basin.. During this fellowship you will engage with research to extend existing computer models of surveillance traps for invasive insects
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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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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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theoretical statistics, statistical machine learning, high-dimensional inference, empirical process theory, stochastic modeling, optimization, or related areas. • Experience with writing and developing