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Field
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extraction, manufacturing, and recycling, are underexplored. This project aims to enhance sustainable machine design through life cycle assessment (LCA), focusing on environmentally friendly materials and
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sectors like aerospace, healthcare, and manufacturing. The convergence of AI with fault-tolerant design principles is transforming traditional maintenance paradigms, leading to more robust and intelligent
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your
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climate states. Key responsibilities To carry out and complete your own research towards a doctoral degree. Design, run, and analyse equilibrium simulations with a coupled Earth-system model of intermediate
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values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your
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systematic phases: first, establishing a robust data foundation; second, designing a modular and open digital platform; and third, developing a predictive maintenance application as a proof of concept
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values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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of the thesis project, written and oral proficiency in English, the capacity for analytical thinking, the ability to collaborate, as well as creativity, initiative, and independence. The assessment will be based