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Field
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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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Previous Job Job Title Post-Doctoral Associate - Measurement and Manipulation of Developing Cortical Networks Next Job Apply for Job Job ID 370353 Location Twin Cities Job Family Academic Full/Part
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will be responsible for programming and maintaining gait exoskeleton systems, developing and implementing real-time control algorithms in C/C++, Python, and Simulink, as well as integrating feedback from
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properties of macromolecules, developing novel ways to combine quantum chemical methods and machine learning, developing quantum algorithms for computational chemistry on quantum computers, and applying
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learning algorithms and creating predictive models. This role will contribute to evidence-based processes and procedures for program quality review and assessment of outcomes data as well as develop
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calculations Expertise in one or more of the following: computational methods, software and algorithm development, high-performance computing, and data analysis Proficiency in relevant programming languages (e.g
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on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position collaborates with faculty, students, and external partners
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executing the experiments Developing relevant algorithms for experimental data post-processing Mentor and work closely with multiple Ph.D. and M.S. students as well as four principal investigators from
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incumbent will conduct research within the predetermined research scope and methodology, and conduct research in the fields of quantum-engineered power grids and AI-enabled smart grids. ● Develop
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the Pacific Northwest and developing a time-dependent interseismic coupling model. This project is a component of an active research grant funded by the National Science Foundation for the Cascadia Region