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) applications. You will specifically apply these methods to dynamic reservoir modeling, area of review evaluation, and wellbore integrity assessment of GCS in both new fields and reuse scenarios. You will also
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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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for distributed AI training, agentic AI with modeling and simulation, and end-to-end workflow monitoring, profiling, and optimization. Working with quantum simulation tools, including NVIDIA CUDA-Q, to enable
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in one or more of: Python, C++, Fortran, Julia. Hands-on experience building and training AI models with frameworks such as TensorFlow or PyTorch. Ability to succeed in an interdisciplinary team and
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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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: 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
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and Berkeley Lab. Alvarez Fellows apply advances in computer science, mathematics, computational science, data science, machine learning or AI to computational modeling, simulations, and advanced data
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computing. Model and analyze Rydberg-mediated interactions for both single-atom and ensemble qubits. Analyze optical trapping and transport dynamics of single atoms and ensembles. Evaluate and benchmark