13 data-"https:"-"https:"-"https:" research jobs at Lawrence Berkeley National Laboratory
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for data analysis and predictive modeling. The fellow will join a small, world-class, multi-institutional team advancing microelectronics research through AI-enhanced methodologies. You will: Perform soft X
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-tuning and/or Retrieval-Augmented Generation (RAG) methods to augment LLMs with dedicated knowledge in transportation and electric grid domains. This involves designing methods to process input data and
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design and implement mitigation strategies. Validate, analyze, and interpret experimental data. Firmware development & programming tools to interface with FPGA-based electronics. Quantum verification and
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the scanning tunneling microscope. Prepare and characterize 2D material heterostructures in support of optical and thermal experiments. Automate parts of sample fabrication, and data analysis to improve
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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
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and contact information for three references. PDFs of 2 top publications (in prep is OK). Additional information: Application date: Priority consideration will be given to candidates who apply by 12/31
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interfaces for thermal studies. Analyze data and automate repeatable parts of fabrication, measurement, and post-processing (Python). Maintain and improve experimental setups; coordinate repairs, vendor
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data. Communicate results through peer-reviewed publications, technical reports, conference presentations, and sponsor engagement. Build collaborative networks across Berkeley Lab and the DOE national
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the development and application of a digital twin approach, aimed at bridging the gap between theoretical predictions, data interpretation, and experimental/spectroscopic observations. What You Will Do: Design
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of Perlmutter and connect directly to DOE experimental and observational facilities so research teams can stream and analyze data in near real time. You will collaborate with NERSC staff, domain scientists, and