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Field
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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and magnetic fields. A crucial aspect lies in the interpretation/modelling of the results. In this role, you will get to work with a variety of devices structures, with an emphasis on quantifying
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Engineering Description Key areas of research include: Scenario generation with LLMs to expose hidden failure modes in E2E models. Embedding regulatory, behavioral, and environmental knowledge into generated
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14 Oct 2025 Job Information Organisation/Company Empa Research Field Computer science » Programming Computer science » Other Engineering » Electrical engineering Engineering » Other Mathematics
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PhD studentship: AI-Driven Fall Detection from Floor Vibrations Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project
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are offering four PhD positions in the Simulation and Data Lab Digital Bioeconomy (SDL-DBE). The SDL-DBE develops and applies multiscale models, AI-enhanced simulations, and computational workflows across IBG
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experimental chemistry. Additional training in a wide range of soft and hard skills is available at the University. What you will do: This PhD project aims to bring together multi-scale modelling and data-driven
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systems modeling, or an [electrical] engineering background, but with an interest (or prior experience) in computational intelligence, AI and/or agent-based modeling techniques. He or she will work closely
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models