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Field
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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how circuit structure supports computation. Will lead research on one or more of the following areas: Automated proofreading & annotation at scale: Machine learning approaches for error detection, human
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Python and GEE for geospatial analysis and data science. Experience with machine learning/deep learning frameworks (e.g., PyTorch, TensorFlow) applied to image or geospatial data. A track record of first
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, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs. Proficiency inPython programmingand major ML/DL frameworks (e.g., PyTorch, TensorFlow
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think outside the box, to learn fast, collaborate effectively, iterate quickly, and work at the interface of both experimental and computational design. Qualifications for Computer Scientists, AI/ML: PhD
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students. Qualifications: Required: PhD in Computer Science, AI, Data Science, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs
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regulatory compliance Strong computer programming skills, including Python and Git/Github Experience or interest in learning data visualization strategies and dashboard development using tools like Streamlit
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Research Associate. The Goodwill Computer Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain