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performance PhD enrolment: Technical University of Denmark DC7: High-resolution waste-derived novel biotechnology feedstock-host background data generation to enable sustainability assessments PhD enrolment
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a research grant to support project costs. What you will gain: A high-quality PhD project embedded in a wider programme with strong academic and industry engagement - projects and training are co
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Minimally invasive interventions increasingly require tools capable
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
to cutting-edge facilities including High-velocity impact testing, Advanced composite manufacturing labs, X-ray computed tomography and High-performance computing resources for AI model training This project
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supportive international research environment with English as the primary working language Access to state-of-the-art infrastructure and high-performance computing Dedicated training programs to enhance your
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to design and evaluate Artificial Intelligent (AI) systems with and for people, aligning AI technologies with human needs and values, situated at the intersection of AI and Human-Computer Interaction
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an interdisciplinary and international team and to regularly present research findings Skills in materials chemistry, actinide chemistry, theoretical chemistry, high-performance computing, and programming are beneficial
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morphology with its performance (reactivity, selectivity, efficiency) and degradation under realistic long term deployment conditions Coordination and execution of in-house beam times Collaboration with other
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found here: https://ut.ee/en/home Subject area and duties: The candidate for the PhD position is expected to conduct research on the synthesis of novel monomers and polymers for high-performance
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collaboration with a leading high-tech semiconductor manufacturing company, demonstrating how edge devices can execute foundation models with guaranteed performance and minimal energy footprint—closing the gap