30 phd-position-in-data-modeling-"Prof"-"Prof" positions at Lawrence Berkeley National Laboratory
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engage in AI/ML-based inverse modeling for the analysis of well test and fluid-flow experimental data, the development and deployment of AI/ML models into software tools and programs, and performance
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tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics, or a related numerical field. Programming experience
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for near-real-time data analysis. Your work will help 12,000+ users run faster, more reliable science. What You Will Do: Contribute to one or more NESAP scientific workflows targeting NERSC HPC resources
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. Work with model cell systems including H-cells and interdigitated electrodes to isolate specific electrolyte/EDL effects. Perform basic characterization such as XPS, XRD, SEM/EDS, FIB-SEM, TEM etc. and
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teams by transforming conceptual ideas into detailed mechanical designs. Your responsibilities will include creating precise 3D CAD models and 2D fabrication drawings to support the manufacturing
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-design validation experiments with experimentalists; iterate models using feedback from new measurements. Automate the workflow: Build Python workflows for simulation and data processing, including HPC job
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(deep learning, generative models, NLP, computer vision) and integrate these approaches with Lab-scale HPC and cloud environments. Develop and optimize pipelines that combine HPC with AI frameworks
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other acceptable form of identification is required to access Berkeley Lab sites (for more information click here ). This position will be cleared to level Q. Applicants selected will be subject to a
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insights for high energy particle physics. This position will contribute to the development and/or advancement of open-source software tools. This position will be involved in the design and execution
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: Experience with fitting parametric models to neural population data. Experience with high-performance computing. Notes: This is a full-time, 2 years, postdoctoral appointment with the possibility of renewal