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Field
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About the Opportunity Job Summary The Postdoctoral Associate will conduct integrated experimental and computational research on informatics-driven mechanical characterization of solid and porous
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autonomous laboratories. The broader goal is to enable AI-driven materials discovery, autonomous synthesis, and the development of high-quality, reusable datasets that support adaptive experimentation and
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with Weights & Biases, MLflow, or TensorBoard Apply data engineering and processing tools such as Pandas, NumPy, spaCy, scikit-learn, and NLTK. Develop model evaluation frameworks incorporating both
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-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross
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Position Summary The successful candidate will work with Dr. Kunlun Qi < https://kunlun-qi.github.io > on research projects related to kinetic equation and its related multiscale model reduction
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organizations. The work will take place in a collaborative, mission-driven research environment that values technical creativity, rigorous engineering, scientific impact, and teamwork. The group works
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industrial partners across Europe. Your primary tasks will be to: Develop and validate a fully coupled hydro-aero-servo-elastic floating offshore wind turbine model in OpenFast. Develop data-driven
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-dependent interactions learnable and predictable. At the centre of the project is ARCA (AI-guided Root microbiome engineering for ClimAte-resilient and nutritious crops), a crop microbiome foundation model
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and
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), and data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems. Major Duties