35 machine-learning-"https:"-"https:"-"https:" positions at Lawrence Berkeley National Laboratory
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Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher -- Scientific Machine Learning (NESAP) to join the Workflow Readiness team as part of NERSC's
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studies on quantum materials and complex oxides, with an emphasis on microelectronics applications. In addition to experimental work, the role includes applying machine-learning and AI-based approaches
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wide range of numerical and machine learning (ML) computer algorithms as applied to reservoir engineering and geophysical imaging. This includes the simulation of thermal-hydro-mechanical-chemical (THMC
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Mandatory 3 Years of Postdoctoral research experience or equivalent research experience. Past Experience in either Machine learning accelerators or SRAM array design or basic blocks of processor at transistor
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recognized four year apprenticeship program in plant maintenance or completion of an accredited two-year program with two years of work experience; or an equivalent combination of documentable military or
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instrumentation and Software development experience in a multidisciplinary environment. Desired skills/knowledge: Working knowledge of Machine Learning methods applied to scientific data and self-driving laboratory
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phases of the scientific lifecycle, supporting the efficiency and effectiveness of capabilities for data analysis, data management, data storage, computation, machine learning, and related IT needs
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experience in Bioinformatics or an equivalent combination of education and experience. Experience with Jupiter Notebooks, including database structure and management. Experience applying machine learning
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complex electronic systems. Able to work independently and exercise sound technical judgment. Familiar with equipment design principles and techniques. Proficient in standard computer applications
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accessible gate-based quantum computer. Our technology platform is based on superconducting quantum circuit processors, and we aim to generate the detailed experimental findings needed to resolve foundational