53 Environment Postdoctoral positions at Oak Ridge National Laboratory in United-State
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environmental science, environmental chemistry, civil/environmental engineering, biogeochemistry, biology or related discipline. Knowledge and experience in laboratory environment. Ability to articulate a
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manner. Ensure compliance with environment, safety, health, and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Deliver ORNL’s mission
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). Demonstrated expertise in data preprocessing pipelines, AI-ready dataset design, or scientific workflows in HPC environments. Proven experience with modern data frameworks (e.g., PyTorch, TensorFlow), scalable I
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the nation’s most pressing challenges. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued
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)geochemistry, soil science, or a related discipline. Experience with laboratory investigations and experimental design. Prioritizes a safe work environment and complies with all ES&H regulations. A proven
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of tackling the nation’s most challenging scientific problems. With a dedicated staff of over 6,000 people, ORNL advances breakthrough research in energy, environment, computing, and national security
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simulations for fermionic and Hubbard-like materials models • Collaborate within a multi-disciplinary research environment consisting of quantum computing experts, computational scientists, and condensed
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(Xilinx Vitis/Vivado, Intel Quartus, HLS tools) HPC environments or GPU-accelerated computing On-detector firmware or data acquisition systems Familiarity with HEP data formats and reconstruction
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. Our team is made up of over 7,000 dedicated and innovative individuals! Our goal is to create an environment where a variety of perspectives and backgrounds are valued, ensuring ORNL is known as a top
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, multidisciplinary team environment. Preferred Qualifications: Knowledge of uncertainty quantification methods and causal inference for complex environmental systems. Experience with large-scale Earth system