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Expansion: Implementing the next phase of the project to transition from rigid-body models to sophisticated systems for protein ensemble modeling. Computational Optimization: Resolving hardware-specific
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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design and execute experiments using lung cancer and lung infection Organ Chips. Develop, optimize, and characterize human lung microphysiological models for translational studies. Analyze and interpret
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design and execute experiments using lung cancer and lung infection Organ Chips. Develop, optimize, and characterize human lung microphysiological models for translational studies. Analyze and interpret
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magnetic response. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed
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(neutral and doped quantum dots in micropillars, NV centers in waveguides, optical nonlinearities, etc.). Approaches may be analytical, based on the collision model, or numerical. The postdoctoral researcher
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-accurate-portable-diagnostics/. What you’ll do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries
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renewable energy. Responsibilities include calibrating simulations with experimental/numerical data, performance analysis, and contributing to interdisciplinary research on coastal protection, with
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do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries to improve sensor performance