14 phd-machine-learning positions at Lawrence Berkeley National Laboratory in United States
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medium and Lyman alpha forest with the Nyx code. Harness the power of artificial intelligence and machine learning to analyze massive datasets and enhance scientific inference. This role will play a
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areas for development, implementation, and evaluation of (1) advanced FDD and control tools (including both model predictive control (MPC) and machine-learning) for building HVAC and district energy
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nuclear structure research within the group. Additional Responsibilities as needed: May mentor PhD or undergraduate students. Other duties as assigned. What is Required: Ph.D. in Nuclear Physics/Chemistry
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of experimental results for publication in peer-reviewed journals. Actively contribute to ongoing efforts in nuclear structure research within the group. Additional Responsibilities as needed: May mentor PhD
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demonstrated experience. Additional Responsibilities as needed: Plan and conduct research to develop machine learning techniques for the tuning and performance optimization of ECR ion sources. Contribute
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Responsibilities as needed: Plan and conduct research to develop machine learning techniques for the tuning and performance optimization of ECR ion sources. Contribute to VENUS's overall software development
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research on topics related to machine learning, data analytics, and thermal resilience modeling. You will also support maintenance of building energy software tools such as OpenStudio measures, CBES, and
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for the group and the division. May supervise metrology laboratory staff. What is Required: PhD or equivalent in years of relevant work experience in relevant scientific or engineering disciplines, as
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ultrafast laser systems. Experience with machine learning. Excellent verbal and written communication skills. Salary: The monthly salary range for this position is $5,374-$7,510 and is expected to start at
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division. May supervise metrology laboratory staff. What is Required: PhD or equivalent in years of relevant work experience in relevant scientific or engineering disciplines, as demonstrated by a broad