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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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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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. Experience with modelling and simulation of complex, interdisciplinary systems Knowledge of computer architecture, computer networking, and experience with the development of virtualization technologies and/or
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seeking a Geospatial Data Engineer to support research and operational workflows focused on scalable geospatial data science, applied machine learning, and production-grade engineering practices to deliver
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the following requirements: Contribute to and progressively lead Artificial Intelligence (AI) & Machine Learning (ML) engineering support on an ongoing basis as evidenced by, for example, innovative artifacts
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, electrical engineering, mechatronics, robotics, computer engineering, computer science, or a closely related discipline. Working knowledge of machine learning and deep learning models, including
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in urban-scale building energy modeling, software development (esp. Python), or Artificial Intelligence/Machine Learning (AI/ML) Strong ideation, writing, and communication skills for establishing
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strengths in any of these areas — quantitative imaging, modeling/transport science, machine learning, or scientific programming — are encouraged to apply. Major Duties/Responsibilities: Lead energy‑storage
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modeling, multiscale approaches) to support materials development and manufacturing process understanding. Use AI, machine learning, and data-driven methods as enabling tools to accelerate experimentation
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from