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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 9 days ago
evaluation, active ingredient sameness demonstration, and post marketing safety surveillance. This Division coordinates modeling, simulation, data analysis and data mining, and establishes the scientific
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practice (e.g., Python/MATLAB/C++ and/or established modelling platforms). Familiarity with asymptotic and multiscale mathematical analysis methods to ground proof numerical simulations Strong communication
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characterization and theoretical modeling. Key Responsibilities: - Develop DLW fabrication workflows for hybrid photonic structures. - Integrate plasmonic nanoparticles and quantum emitters; characterize optical and
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-based modelling and design of microwave and RF devices. Perform electromagnetic simulation, design, fabrication, and experimental validation of microwave and RF components. Develop physics-assisted AI/ML
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implementation of a coupled Computational Fluid Dynamics (CFD) and fire dynamics model within the OpenFOAM framework. This model will simulate battery thermal runaway and subsequent fire propagation. The
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . Key Responsibilities: The Research Fellow will be in charge of modelling, simulation, and analysis for projects
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, the research participant will help develop a process-based crop model designed for watershed scales to address this limitation. This will be achieved by coupling an existing crop simulation model with
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systems, with a focus on thermal integration of metal hydride storage systems. The Research Fellow will develop CFD models, process simulations, and system-level integration strategies to enable efficient
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surrogate model for giant impacts trained on SPH simulations, and then to analyse the results in the context of the geophysics, geochemistry and dynamics of the terrestrial planets. For more information and
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laser experimentation to study and control complex nonlinear dynamics. Key Responsibilities: Develop and implement Physics-Informed Neural Network (PINN) models to simulate, predict, and analyze complex