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Field
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distributions. We wish to represent the biological networks into proper formats, e.g., vector representations, so that existing machine learning algorithms (e.g., support vector machines) can readily be used
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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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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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-intensive, often being non-interpretable, and being highly brittle towards shifts in data distribution and changes in the problem domain. This fully-funded three-year project, with the possible further
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Qualifications: Education: BS in Geospatial Data Science, Geographic Information Science, Computer Science or close equivalent. Experience: Experience developing and optimizing algorithms. Extensive programming
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of distributed MIMO, and/or coordinated multi-AP operation (under study in the Wi-Fi 8 standardisation workgroup), using Hardware Description Language on FPGA, based on the open-source openwifi project (https
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investigation and developing software algorithms and techniques to support human or machine information interactions for the purpose of information retrieval/dissemination, analysis and/or decision making
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of acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text
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algorithms) that enable rapid creation of new high-fidelity multi-scale/multi-physics computer models of materials capable of utilizing modern extreme-scale computing environments. The success of multi-scale