53 coding-"https:"-"Prof"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "U.S" "St" "St" "St" Postdoctoral positions at Oak Ridge National Laboratory
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Requisition Id 15598 Overview: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges
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scientific outputs that may include peer-reviewed publications in top-tier water journals, professional scientific code/software contributions, and high-quality datasets. Candidates must also be willing
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the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once
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coding (Python) for building energy modeling and controls Preferred Qualifications: Expertise in modern optimal control techniques (e.g., AI based controls) High level of competence in coding and scripting
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funded by the U.S. Department of Energy (DOE) Office of Basic Energy Sciences (BES) in the Materials Sciences and Technology Division (MSTD). The successful candidate will be expected to work effectively
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those skills to a variety of problems, and the ability to determine and understand the broader context of his or her research. Preferred Qualifications: Proficiency in multiple modern coding languages is
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candidates: If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk
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the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment. Once
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testing. About ORNL: As a U.S. Department of Energy (DOE) Office of Science national laboratory, ORNL has an impressive 80-year legacy of addressing the nation’s most pressing challenges. Our team is made
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
familiarity with AI/ML algorithms, for generative materials design, or for knowledge extraction, e.g. causal ML or symbolic regression, etc. Strong demonstrated background in coding for data analysis using