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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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-to-design mapping, mixture density networks or MDN-based inverse generators will be employed to capture the multimodal distribution of the design space, enabling flexible inverse design sampling based
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distribution and with synthesised future sea-ice distributions; examine the impacts on barrier wind structure and associated surface turbulent fluxes. Examine the frequency, characteristics and ocean mixed-layer
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physics all the way to numerical simulation algorithms? Then apply now to join our team of researchers in the Quantum Information and Quantum Many-Body Physics research group. Your personal sphere
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
distributed infrastructure Applications of agentic AI What You Will Get An inclusive and open-minded research environment that bridges theoretical thinking and system building, with opportunities to publish and
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framework to accommodate new components like EV chargers and distributed generation, and you will apply these insights to both large-signal and small-signal stability analyses. Working closely with academic
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like EV chargers and distributed generation, and you will apply these insights to both large-signal and small-signal stability analyses. Working closely with academic and industrial partners, you will
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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-critical systems. The research will focus on developing AI-powered verification tools, health monitoring algorithms, and compliance assurance techniques that ensure system reliability throughout
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focuses on the analysis of neuronal networks in the Drosophila brain, and the Hummel team currently consists of postdocs, pre-docs, master students and administrative colleagues who share a common interest