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science applications Computational Atomic-scale Materials Design with a focus on materials modeling and discovery with electronic structure calculations and machine learning Luminescence Physics and
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converging operational research, machine learning, and decision-making methodologies. The ultimate goal is to create real-time autonomous systems that are not only trustworthy but also adaptive in the face of
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will contribute to, and lead, include: Building and operating ultra-high vacuum and laser systems. Building electronics and automation schemes. Learning/operating fabrication and characterization
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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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Small case Studies of Successful Innovation Funding Methods The project will employ a combination of mainly quantitative methods, including machine learning (ML) and generative AI (GenAI) models
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activities of the IKE research group, but will, as part of the GreenTraC-project, also join a cross-organisational and interdisciplinary team working on developing and implementing machine learning and natural
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internet Quantum embeddings for machine learning Networked quantum sensing supported by distributed classical communication Prospective applicants to this PhD proposal should have the following
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defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and enable charge