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Field
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Finite Element Model of the Larva Body: Utilise existing Drosophila larva CT-scan data to segment components such as the cuticle, muscles, and mouth hook. Implement finite element simulations within
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and the effects of disordered correlated microstructures on diffusion; iii) development of energy-based models and numerical simulations of hyperuniform assemblies; iv) development and application
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simulation framework to model the coupled USV-UUV system, enabling safe experimentation before field deployment. Field validation: Conducting field experiments (e.g. in a harbor and offshore test site) where
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: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
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, evaluating, and fine-tuning machine learning models (e.g. deep neural networks) to segment underwater scenes and classify anomalies. The work will explore the use of virtual environments and synthetic datasets
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. Integrate hydraulic-hydrologic modeling and surrogate models (e.g., Bayesian Networks) to simulate stormwater behavior under future scenarios. Apply optimization techniques to design and evaluate nature-based
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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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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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how variations in mould structure, porosity, and surface characteristics affect radiative heat transfer and casting performance. Phase-field modelling will also be used to simulate defect formation and