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Field
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algorithms that can tightly integrate physical hardware, sensors, and computational models. This PhD position is centered on addressing these challenges through innovative computational methods, combining
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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of microstructures, physics-oriented microstructure descriptors discovery based on the use of RRAE (rank reduction autoencoders) and neural operator approaches designed to approximate the solution of complex
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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the partner teams and a theory/experiment dialog will allow to enrich the project. The person recruited will have the opportunity to use a large panel of theoretical chemistry and molecular modeling methods
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from computational engineering and computer simulation (such as the finite element method and constitutive materials models) to represent and exploit relationships along the composition-process-structure
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 17 days ago
abstract representations. The FAMIX metamodel of the Moose platform (Anquetil et al. 2020) models software systems as entities (classes, methods, attributes, etc.) and relationships (calls, inheritance
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 23 days ago
fundamental physical mechanisms using computer simulations and the methods of non-equilibrium statistical physics. The Research The PhD student will work under the supervision of Dr. Philip Bittihn within
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, artificial intelligence, and its applications to large scale data domains in science and industry. This includes the development of deep generative models, methods for approximate inference, probabilistic