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- Zintellect
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Field
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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, or a closely related field. Preferred Qualification: Research experience in biomedical implants, animal models, and aging/Alzheimer’s disease. Research experience in biomaterials, neuroscience
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of parallel synthesis and purification methods is required. Some experience in molecular modeling and/or protein-inhibitor visualization is a plus. The incumbent must be able to synthesize, purify, crystallize
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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
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theories. Develops advanced analytical models and systems and provides solutions and analyses to support strategic and tactical decisions. Adheres to University and unit-level policies and procedures and
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working in radiological environment. Experience in heat transfer and thermal modeling/simulation using finite-element analysis (FEA) or other software. Ability to work within a multi-disciplinary team
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at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
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. Key responsibilities include: Simulating regional air quality using numerical models (e.g., WRF-Chem, CMAQ) Improving wildfire emission estimates and smoke plume rise representation Evaluating model
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on ab initio data to accurately model the thermodynamic and thermophysical properties of complex materials. Conduct molecular simulations to elucidate the thermodynamic and structural basis of enhanced
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-funded R01 project to develop and disseminate computational neuron models and their integration with TES and TMS electric field simulations in the popular SimNIBS software package. This project is a