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of Materials, analytical and numerical Data-Driven Engineering Design and Optimization Algorithms Surrogate Modeling (e.g., Kriging, Gaussian Processes, Neural Networks, etc.) Scientific Programming (e.g
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systems. By combining microclimate modelling, remote sensing data, and data-driven methods, the results are integrated into a Digital Twin framework. The research will support predictive risk assessment and
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relevant if there is a strong focus on data-driven modeling, machine learning, and control. In any case, a documented background or experience in control is required. Your education must correspond to a five
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innovative approaches in bit technology, hydraulic hammer systems, drilling fluids, and thermal management. The project will combine experimental insights, physical modeling, digital‑twin technologies, and AI
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25th April 2026 Languages English English English The Department of Marine Technology has a 3-year vacancy for a PhD Candidate in Integrated wind turbine modelling tools for drivetrain health
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drilling, drilling technology, thermomechanical processes, and AI‑driven drilling optimization, as described in the project outline. Develop and apply numerical, analytical, and data‑driven models
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. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning engines, and reinforcement learning—can be adapted and extended for maritime design
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AI technologies—foundation models, generative design tools, agent platforms, reasoning engines, and reinforcement learning—can be adapted and extended for maritime design challenges. The final research
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approaches for identifying, modelling, and integrating uncertainty factors originating from IoE devices and system dynamics, combining data-driven learning with knowledge-based modelling techniques
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract