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for the collection of in-situ mechanical testing data. Knowledge of scientific software in python (numpy, scipy, pandas) or related data analysis suite. Record of productive and creative research as demonstrated by
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data assimilation systems on Unix/Linux and high-performance computing platforms. Evaluate model and assimilation performance using statistical and dynamical diagnostics and verification against
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community electrification and decarbonization. Understanding for creating, modifying, and analyzing OpenStudio and EnergyPlus building energy models. Strong software development skills for automation of many
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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, and bioactive compounds from plant, food, microbial, and human samples; develops and optimizes analytical methods; analyzes complex datasets using metabolomics and statistical software tools
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statistical software & machine learning (e.g., R, Python, SAS, or STATA). Experience working with large population dataset (e.g. EHR, claims data). Background in health informatics, population health research
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understanding of parallel application development techniques (parallel programming models, algorithms, and software) Preferred Qualifications: Experience in implementing ab initio simulation codes such as VASP
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development, intelligent software architecture and design, automated testing and verification, code generation and repair, DevOps and deployment automation, software maintenance and evolution, software
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, collaborate with interdisciplinary teams, publish in leading scientific venues, and contribute to emerging QHPC software and system technologies. Major Duties/Responsibilities: Develop and evaluate
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-throughput phenotyping at ORNL's Advanced Plant Phenotyping Laboratory (APPL) with paired geochemical and microbial measurements. Utilize laboratory equipment and software that may include a Picarro gas