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03.06.2021, Wissenschaftliches Personal The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and
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03.06.2021, Wissenschaftliches Personal The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and
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scientific work on design automation for quantum computers and develop methods and software tools dedicated to the design and realization of quantum algorithms/circuits. One of the main challenges in
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develop methods and software tools that aid the design of microfluidic devices (also known as Labs-on-a-Chip). While these devices are mainly designed manually thus far, we investigate methods
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develop methods and software tools that aid the design of microfluidic devices (also known as Labs-on-a-Chip). While these devices are mainly designed manually thus far, we investigate methods
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computer science with very good results - Interest on topics around the area of distributed systems and data management - Basic knowledge in distributed systems and graph algorithms is desired - Hand-on experience
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of Munich (www.cda.cit.tum.de ). Accordingly, we are currently searching for a Ph.D. Student to join our team to work on Design Methods for the European Train Control System! Our Research The European Train
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for quantum computers and develop methods and software tools dedicated to the design and realization of quantum algorithms/circuits. We see ourselves as an interface between the stakeholders building physical