66 modeling-and-simulation-"UNIVERSITY-OF-SOUTHAMPTON" Postdoctoral positions in Sweden
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- Chalmers University of Technology
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-of-the-art computational resources, enabling advanced simulations and modeling. Moreover, we actively collaborate with leading experts in laser diagnostics, automatic control, heat transfer, and life cycle
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We are seeking a highly motivated person to conduct research in economic modelling of forest resources to support analysis of environmental and climate change issues. About the position The project
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design. One of our group's goals is to create efficient surrogate models that reduce the computational cost of MD simulations by several orders of magnitude. Notable examples of our work in this area
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on close collaboration between the university and industry and aims to optimize processes, reduce error margins and increase productivity in the industrial companies involved in the project. Virtual models
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educational programs, we are now seeking a postdoctoral researcher to work on privacy for data-driven models and high-dimensional data. The position is full-time for two years, starting on 1st September, or as
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models into Chalmers’ bridge simulators in collaboration with other researchers. You are also expected to supervise PhD and MSc students and to publish at least two peer-reviewed journal articles during
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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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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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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