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analysis, and high-performance simulations? We offer a challenging PhD position in the Mathematics of Computational Science group to design and analyse novel structure-preserving methods for stochastic
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conduct their research in the Physics of Living Matter Group, with exciting collaborative opportunities globally, as well as within Luxembourg. The doctoral candidate will perform wet lab experiments as
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the top of the world’s universities in most rankings and among the best universities in the world in certain fields. Our high-quality basic research, a multidisciplinary research profile and close
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Description Ageing infrastructures, urbanization, and climate change are intensifying the vulnerability of critical infrastructures (CI), high-tech industries (HTI), and communities to cascading hazards
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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on creating new catalysts for green chemistry through de novo design of enzymes that incorporate transition metals. The project will engineer and evaluate catalytic proteins that perform difficult
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and quantum states of atoms or molecules prior to the reaction. By combining this with resonance-enhanced multiphoton ionisation and velocity map imaging, we can probe the reaction products in high
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performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators (e.g., Kalman Filter
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relevant lists to ensure compliance with applicable export control and sanctions rules. Candidates will be asked to provide information about affiliation to high-risk countries for a security assessment
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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators