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date. Demonstrated expertise in optimization (e.g., convex/nonconvex, stochastic, combinatorial), probability and stochastic processes, numerical linear algebra, and algorithm design. Proficiency in
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Responsibilities: Build and analyze dynamical system models (multiscale, QSP, PBPK, PK-PD). Apply numerical methods, optimization, and parameter estimation to calibrate models to experimental/clinical data. Perform
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. The researcher will also design data-driven numerical approaches to address a variety of real-life optimization models including disaster and emergency logistics, supply chain and transportation. Specific
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-of-the-art experimental techniques, innovative methodologies, and/or numerical models to make fundamental advances to existing literature. Duties will include but are not limited to: Helping develop novel
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(multiscale, QSP, PBPK, PK-PD).Apply numerical methods, optimization, and parameter estimation to calibrate models to experimental/clinical data.Perform sensitivity and uncertainty analyses to assess robustness
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characterization of germanium detectors, develop advanced Monte Carlo models and AI-driven analysis tools to optimize detector response, and to promote precision medical imaging and low energy dark matter searches
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date). Strong background in power systems analysis (OPF, state estimation) and numerical optimization/control. Proficiency with Python/MATLAB and power-system toolchains (e.g., MATPOWER/OpenDSS
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systems. The individual will be responsible for: • Develop and implement models for the structural and mechanical performance and optimization of mass timber systems, using data-driven approaches
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project aimed at utilizing 3D physics-based wave propagation simulations up to 10 Hz to improve ground motion estimates in urbanized areas in California. The central goals are to unify and optimize
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and