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research school on secure distributed computing (SeDiC) is proposed. SeDiC aims to tackle the challenges of exchanging and computing data across a network of interconnected systems. It addresses scalability
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Force Microscopy (SKPFM) for visualising the distribution of cathodic phases in bulk and on plane, along with integral and localized electrochemical measurements for degradation performance. A range of Mg
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and forms of innovative activities Effects of Innovative Change: Allocative and distributive effects Political and institutional dimensions Dynamics and Patterns of Innovative Change: Economic and
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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promising lean alloy system for additive manufacturing, as the mechanical properties can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried
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- and nano-tomography at DESY (Deutsches Elektronen-Synchrotron / German Electron Synchrotron) and Scanning Kelvin Probe Force Microscopy (SKPFM) for visualising the distribution of cathodic phases in
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Group (EASE IRTG), Empowering Digital Media (EDM), the Research Training Group HEARAZ , the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms
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Max Planck Institute for the Study of Crime, Security and Law, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | 2 months ago
stakeholders are a plus; A desire to expand competencies in the areas of statistics and methodology; Experience with project work in distributed teams is an advantage; Interest in behavioral economics and the
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable
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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