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computational algorithms to enable regenerative cell therapies. Now, we are seeking a highly driven postdoctoral researcher to contribute to our ambitious mission. Division The Division of Biomolecular and
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architectures and training algorithms, uncertainty quantification, high-dimensional stochastic systems and high-dimensional partial differential equation systems. Multiple positions available. About the T-5 Group
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Alexandria, Virginia. The focus of these positions will be on quantum computing, quantum algorithms, quantum learning, quantum error correction, and quantum fault-tolerance. The successful candidate will join
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pathways and degradation mechanisms at inorganic–organic interfaces. The position is hosted at the Fritz-Haber-Institut (Berlin) in a close partnership with the MPI Magdeburg, contributing to algorithm
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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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Future-Proof Smart Logistics. It aims to contribute to the realisation of the PI concept by developing advanced machine learning-based decentralised decision-making algorithms. These algorithms will enable
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candidate will design and develop digital twin algorithms for computational fluid dynamics, contributing to innovative solutions in healthcare software systems. This role involves collaboration with research
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that integrate state-of-the-art Large Language Models (LLMs) with novel logic-based multi-robot planning algorithms. This work will be evaluated through simulations and with physical drones in mock search-and
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data