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Field
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modeling framework capable of simulating complex canal-marsh interactions, hydraulic infrastructure responses, and hydrologic variability. By integrating detailed topographic and hydrologic datasets
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targets through the investigation of anti-inflammatory peptides derived fromanimal venoms. The activities involve exploring the biology of the peptides and their in vitroeffects on cellular models
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. Develop simulation environments and digital twins using tools such as Gazebo, NVIDIA Isaac Sim, or Unity to model the CEA environment as well as robotic perception, navigation, and manipulation tasks
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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of the Solar Atmospheric Modelling Suite (SAMS) a next-generation, modular simulation framework for the solar atmosphere, tackling the chromosphere/corona problem. SAMS is being designed to incorporate world
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of reports, dissertations and end-of-study papers. Skills Expertise in the following fields: Chemical and process engineering. Modeling (first-principles and data-driven models), simulation and optimization
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assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
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with the land surface model to conduct regional simulations; Flux and concentration footprint modelling; Post-processing of eddy covariance data, gap filling and data analysis; Plan, coordinate and
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trials based on experimental design. Contribute to the numerical modeling of beneficiation circuits (e.g., using USIMPAC or similar software). Analyze, interpret, and report the outcomes of simulations and
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. The successful candidate will develop advanced machine learning (ML) models to automate and optimize retrosynthetic analysis, facilitating the discovery of efficient and sustainable synthetic routes for complex