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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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, Python, R, Java, or other scientific programming languages. Experience working with life cycle assessment tools such as GREET®, OpenLCA, SimaPro®, GaBi®, or similar. Skilled oral and written communication
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of urbanization on precipitation, and aerosol-cloud interactions Strong modeling skills and high-performance computing experience Experience with model code development, and strong programming skills (e.g., Fortran
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computer vision. Experience with multi-modal data fusion and alignment techniques. Experience with spatial transcriptomics or other -omics data analysis. Proficiency in Python programming and scientific
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(electrochemistry, materials synthesis, or characterization) or computational simulations perspective, is required. Proficiency in Python programming is required. Familiarity with REST APIs is desirable. Master’s
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preferred. Strong data analysis skills and ability in programming languages, such as Python, for performing experiments and analyzing data. Knowledge of innovative phase retrieval algorithms is desirable
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and preparation, and programming for data analysis Good skills in oral and written communication with a record of publication and external recognition Experience with synchrotron x-ray based
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. Strong programming skills in Python, or other relevant languages used in AI/ML. Significant knowledge in machine learning (ML) and applied mathematics. Ability to conduct independent research and
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with physics-informed neural networks, automatic differentiation, neural ODEs, or other physics-aware DL techniques. Skill in programming languages such as Python, C/C++, Go, Rust etc. Ability to model
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programming. Strong oral and written communication skills. Excellent collaboration and teamwork abilities. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred