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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a
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engines. Knowledge of advanced combustion concepts is a merit but not a prerequisite. Practical experience of engine operation in a lab is a merit. Knowledge of control theory is a merit and even better is
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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and colleagues working with theory that will complement your work with density functional theory, phase-field simulations, and finite element modelling. Qualified applicants must have: Enthusiasm
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. During the project, you will work closely together with colleagues in theory who propose optimized designs for your devices, and both receive and provide help to fellow colleagues performing
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understanding of combustion and engines. Knowledge of advanced combustion concepts is a merit but not a prerequisite. Practical experience of engine operation in a lab is a merit. Knowledge of control theory is a
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candidate to help develop these new manners by which to promote bonds as kernels in the interpretation of chemical simulations. For this purpose, novel theory and simulation software will need to be developed
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cement based on literature, theory, and experiments. Optimization of composition with other waste materials. Thermodynamic modelling and experiments with advanced technologies are used. Development of a
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theory, symmetry analysis, and group theory. You will work on developing and applying these ideas to discover new photonic phenomena, implement associated computational tooling, and to find opportunities
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partners and optimize them for nonlinear quantum processes Collaborate with theory colleagues to refine fiber designs based on experimental feedback Disseminate results at international conferences and in