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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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position to develop and apply emulators for Earth System Models (ESMs), with a particular focus on the role of the biological carbon pump in oceanic carbon cycling and climate feedbacks. The research will
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will explore reaction evolution mechanisms for magnesium-based binders, leveraging state-of-the-art experimentation and numerical modelling tools. This position is part of the prestigious Villum Synergy
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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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. Variational Autoencoders, Normalized Flows, Generative Adversarial Networks) Have experience in developing fast algorithms for hard combinatorial optimisation problems. Have some knowledge about stochastic
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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. The position focuses on frequency-domain electromagnetic (FEM) and transient electromagnetic (TEM) methods. The successful candidate will contribute to the development of an inversion framework for the joint
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete