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for the in silico models developed as part of the project to determine the model's confidence limits. Development of AI/ML models based on source data from CFD simulations, in vitro, and in vivo studies
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imaging beyond the limits of conventional voxel-based imaging. The project will involve theoretical and computational work, with particular emphasis on physics-based simulations, biophysical modelling
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behavior, in particular during impact scenarios such as the rebound of a quasi-rigid ball on the foam. Depending on the scale considered, simulations may be performed either on explicitly modeled
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for the Modeling of Biological and Sociotechnical Systems (MOBS Lab) within the Network Science Institute at Northeastern University. MOBS Lab focuses on research projects aimed at developing innovative mathematical
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(ALD) reactors. The range of simulations focuses on finite element methods and can include Monte Carlo based simulation approaches and continuum modelling. Verification of simulation results against
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containment. The prohibitively high computational cost of such simulations necessitates the development of efficient and robust surrogate models for general GCS modeling tasks, especially when inverse modeling
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of oxygen and Earth’s first global glaciation about 2.4 billion years ago. Key responsibilities: design, run, and analyse equilibrium and transient simulations with a coupled Earth-system model
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on simulating nanoalloy structures to create a database for materials characterization. The main tasks include running molecular dynamics and Monte Carlo simulations to model nanoalloys under various
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modeling and simulation to complement physical experiments Preferred) Proficiency in CAD and modeling tools such as SolidWorks for experimental design and apparatus development Additional background in areas
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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to