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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
system health monitoring, and more efficient maintenance planning. Digital twins offer a powerful foundation but must evolve beyond simulation to truly support engineering decisions. This PhD will develop
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, particularly GBS, continuous-variable QC Experience with numerical simulation, statistical estimation, or probabilistic modeling. Programming proficiency (Python, Matlab or C++), especially for numerical
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to understand these dynamics. This project proposes a novel pipeline of ideas to generate tools and techniques to simulate HIV infection dynamics using a multiscale agent-based modelling technique (cells, viruses
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requirement. The applicant must be interested in working in an interdisciplinary research environment. The position includes close collaboration with project bioinformaticians developing genomic-based models
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to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
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simulation in hydrogen production systems. They are expected to contribute to model development, analyze flow behavior, and evaluate the impact of operating conditions on hydrogen production. Strong
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climate states. Key responsibilities To carry out and complete your own research towards a doctoral degree. Design, run, and analyse equilibrium simulations with a coupled Earth-system model of intermediate
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mineral and metal-bearing raw materials more efficiently and to recycle them in an environmentally friendly way. The Department of Modelling and Evaluation is looking for a PhD Student (f/m/d) to work in
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-material capability with a suitable closure model; (2) improved strategy for interface tracking/capturing; (3) very high-speed scenarios with use of nonlinear Riemann-solvers. If time allows exploratory 3D
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems