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tap density and material quality. Develop an advanced optimization and sensitivity analysis framework for accurate parameter estimation. Produce research publications in high-impact journals and
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-phase flow behavior and mixing within the CSTR. These simulations will be used to evaluate impeller performance, analyze hydrodynamic characteristics, and identify key synthesis parameters influencing
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control and state (and parameter) estimation algorithms capable of effectively managing corrupted measurement data, communication constraints and modelling uncertainties. You will be joining the team of Dr
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parameter estimation Knowledge of advanced Bayesian methods and samplers, machine learning approaches to signal processing; additionally other methods such as simulation-based inference Good computing skills
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with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation Implementing machine learning approaches that preserve physical constraints while handling
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Engineering, or related. Strong experience in ODE/PDE modeling and simulation (MATLAB, Python, or R). Experience withnumerical methods, optimization, parameter estimation, and sensitivity and uncertainty
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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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calculation of NMR parameters. - Experience in experimental NMR. - Basic knowledge of electrochemistry, magnetic and electronic properties, and solid-state chemistry. - Proficiency in the necessary computer
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territories. The pedigrees reconstructed in each populations are sufficient to estimate some simple quantitative genetic parameters, but they are incomplete and contain errors, which greatly limits
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month ago
(whether P-wave, S-wave, or combined datasets) in relation to the physical parameters being reconstructed (e.g., Lamé parameters, density, anisotropic properties). The goal is to design an inversion