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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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of innovative models that simulate the intestinal environment and study disease progression. Participate in peer-reviewed manuscripts in top scientific journals and contribute to scientific grant funding
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effort on active research projects and publish and present research results. Research work will focus on Computation Fluid Dynamics (CFD) models involving multiphase reacting flow simulations with emphasis
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experience with larval or pathogen dispersal simulation and connectivity analyses strongly preferred. Knowledge of epidemiological modeling, and proficiency in multiple programming/analytical computer
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), Arabic, Tagalog, Farsi, French, or/and Yoruba. Position Overview Additive manufacturing (AM) is a transformational technology for fabricating complex metallic shapes directly from 3-D digital models
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experience with larval or pathogen dispersal simulation and connectivity analyses strongly preferred. Knowledge of epidemiological modeling, and proficiency in multiple programming/analytical computer
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all applicable University and departmental policies and procedures. The postdoc candidate is expected to: 1) Develop coarse-grained or mesoscopic models of dynamic DNA nanostructures and polymer-grafted
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, energy engineering or a closely related discipline. Experience in modeling & simulation of three-dimensional multiphase turbulent reacting flow applications using 3-D CFD codes (e.g., CONVERGE, OpenFOAM
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the cellular and molecular mechanisms that mediate dietary protein sensing and communication by ovarian and adipose tissues. The candidate will utilize Drosophila melanogaster as a model system and employ a
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application of advanced AI models to AV systems. Minimum Qualifications Candidates must meet the following requirements: A Ph.D. in a relevant field such as Artificial Intelligence, Automated Vehicles, Control