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Field
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assess risks of de-anonymization, including researching current de-anonymization strategies Deploying and benchmarking anonymization methods. Investigation of state-of-the-art methods from cryptography and
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monitoring will be based on real time data streaming from the machine numerical control. The project will cover all the aspects related to the implementation and automation of the tool life cycle management
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. The student through participation in team meetings and training with the team will deploy a numerical model (4SAIL) to study the combined effects of the vegetation-soil system temperature and canopy reflectance
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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This PhD project will focus on developing AI-based methods to accelerate the Swansea University in-house discontinuous Galerkin (DG) finite element solver for the Boltzmann-BGK (BBGK) equation
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efforts to contribute to safer marine operations, we actively explore possibilities to utilize both numerical and machine learning methods to enhance the accuracy and resolution of metocean forecasts. About
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fields with a socio-technical background Interest in data anonymization and/or technology law will be considered an asset Interest in applying methods commonly used in information systems research (e.g
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large city (e.g., access to numerous cultural, recreational, and outdoor opportunities). To learn more about the Department, visit our web site at: www.chhs.colostate.edu/ot/. Search Contact Email
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-dependent source depletion. Reducing uncertainty in groundwater risk assessments through refined numerical methods. Applying the improved model to real-world groundwater contamination case studies. Career
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lack of CFD-grade experimental data for reliable validation of numerical methods. In parallel to the CFD research, the Thermo-Fluids group at the University of Manchester is developing a novel modular