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technical and non‑technical stakeholders. Exercise creative and analytical thinking with diverse and multidisciplinary groups. Basic Qualifications: BS degree in computer programming or related field and at
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and approaches to solve complex problems (e.g., information retrieval/extraction, machine learning/deep learning, networking) Experience working with geospatial data and processing workflows and
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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of AI for science such as: scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You’ll help design, train
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collaboration and coordination across instructor-led training (ILT), computer-based training (CBT), blended learning, qualification, and workforce development programs to promote consistency, alignment, and
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challenges. Our research and development capabilities include radar and optics technologies, radio frequency (RF) communications, computational imaging, artificial intelligence / machine learning (AI/ML
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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processing and data analysis tools relating to extraction of information from dynamic test results. Experience in Machine Learning Algorithms and Data Analytics. Experience in Machinery Health Monitoring and
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and Machine Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML models
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and