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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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Publish scientific results in peer-reviewed journals in a timely manner Participate in project planning and execution Maintain detailed and accurate records Acquire and analyze data in keeping with project
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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-body ab-initio methods for description of electronic, magnetic, and vibrational properties in a range of materials Expertise with artificial intelligence and machine learning approaches will be also
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priorities. Willingness and ability to learn new research areas and contribute effectively with initiative and enthusiasm. Excellent written and verbal communication skills, with a demonstrated record of
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
ML concepts and architectures and hands-on experience with open-source AI/ML packages (such as pytorch, scikit-learn, tensorflow, JAX etc.). Preferred Qualifications: Good grasp of concepts in solid