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. In the ELUD research project, we address the question of if and when learning agents converge to an efficient equilibrium and when this is not the case. ELUD will design new algorithms for computing
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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description You will be 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
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description You will be 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
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decision-making algorithms on real robotic systems operating in unstructured and dynamic environments. This work is connected to the Robotics Institute Germany (RIG) and relates to the thematic cluster
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” and “wet” lab workflows). You will be able to Design, develop and implement algorithms and systems based on foundation models, large language models and/or AI agents for automated scientific discovery
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process A single curricular phase To validate this request, you must validate the form that is accessed at the URL: https://seleccio.uab.cat Any application that is not submitted in this way will not be
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Regularization. We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical
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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
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systems. Design and implement algorithms that enable shared control between human operators and autonomous systems to improve teleoperation performance. Maintain active communication and collaboration with