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Field
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geological field data. You will also design and run high-pressure shear-cell experiments on natural and analogue granular materials at Chengdu University of Technology, develop discrete-element simulations
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preferred (Master’s degree or higher). Skills and Knowledge: Proficiency in programming, traffic simulation, statistical modeling, and machine learning/deep learning techniques. Excellent communication skills
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investigated. 4. Advanced Control and Modelling Multi-modular distributed motor-drive architectures and ultra-fast semiconductor devices present significant control challenges. Research may involve FPGA-based
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Can AI learn to reason like a human - and recognise when it isn’t sure? This PhD tackles that challenge in a high-impact setting: interpreting 3D digital models of rock formations built from drone
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combine ultrafast pump–push–probe experiments with sub-10 fs resolution, finite-element simulations, and quantum models extending the Tavis–Cummings Hamiltonian. The aim is to demonstrate coherent control
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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numerical modelling and programming as well as experimental work is a merit Experience from industry is a merit About the employment Fixed term position 48 months, in accordance with Chapter 5, Section 7
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devices, building a software-supported framework to predict and optimize laser-material interactions, energy transfer, and resulting micro-scaled features during drilling and shaping, and modelling of beam
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. 4. Advanced Control and Modelling Multi-modular distributed motor-drive architectures and ultra-fast semiconductor devices present significant control challenges. Research may involve FPGA-based