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finite element modeling as the foundation of a comprehensive design framework that integrates simulation, experimentation, and machine learning. Fall-related injuries are a leading cause of morbidity and
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with industry, academia, authorities, and insurance companies. Main responsibilities Computational Modeling and Simulation Develop and validate finite element models using LS-DYNA, OpenRadioss
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patterns of genomic sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models
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sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models that unlock
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is conducted primarily in the fields of quantum many-body systems, which includes theoretical research in nuclear physics, nanometer physics, quantum information, atomic physics, and modeling
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. Eran Elhaik to design machine-learning models that unlock the potential of genomics for forensic investigations and historical reconstructions. Work duties We aim to develop machine learning methods
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improve models and codes for assessment of the liquid source term during severe accident (SA) of LWR systems. In particular, research will include assessment of the characteristics of the containment water
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to methods and principles aimed at understanding and modelling the mechanics of deformable bodies. Solid mechanics is a core discipline in mechanical engineering and is of fundamental importance to many other
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epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
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reacting flows. A novel aspect of the project is the use of highly perturbed laminar flame simulations to inform CFD modelling of turbulent combustion in lean hydrogen-air mixtures. Experimental work will be