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, and deployability of deep learning models on resource-constrained edge platforms. The PhD candidate will collaborate closely with international project partners and contribute to advancing next
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candidates within Modelling Strength and Failure in Recycled AluminiumAlloys funded through the Centre for Research-based Innovation SFI FAST – Future Aluminium Structures. The positions are linked
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on secure and trustworthy data sharing. The development and refinement of maritime AI models depend on access to large amounts of high-quality operational data. However, data sharing across organisational and
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materials, including porous dimension, building ingredients, wettability, etc., and their interactions with water and gas species include CO2, Hydrogen and methane. Using atomistic modeling, the study will
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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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stem-cell-based model system recently established in the group. We are looking for a highly motivated PhD candidate with some experience in working with stem-cell based models, CRISPR-based gene editing
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. Digital twin technologies offer a promising approach for modelling and monitoring such complex systems. However, the ongoing energy transition also introduces significant uncertainty due to fluctuating
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models, and collaboration mechanisms to ensure efficient, reliable, and sustainable use of shared offshore energy resources. The successful candidates will be a part of a dynamic and internationally
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industry in both acoustics and solar energy. You will gain expertise in experimental acoustics, advanced numerical modelling, sustainable building technologies and performance optimization. Are you motivated
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advancing resilient and sustainable infrastructure? As a PhD Candidate with us, you will work toward earning your doctorate while developing strong expertise in probabilistic modelling, structural reliability