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algorithms for problems related to information markets involving multiple rational autonomous agents. The objectives include: the development of learning algorithms; the study of their theoretical properties
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 1 month ago
Modelling. Your tasks # Development and implementation of numerical and algorithmic methods for the simulation of fluid-dynamical or environmental systems # Research on quantum and hybrid quantum–classical
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algorithm to reduce noise in dual-tracer PET imaging and improve the quantitative accuracy of dual-tracer PET scans. Your Job Simulation of PET data using existing simulation packages. Development
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, immunology, and allergology to better understand the interaction between airway epithelium and mast cells within healthy and diseased airways, identify biomarkers, and develop algorithms for the diagnosis and
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, Intelligent_Mapping is integrated into the IRIMA Plateformes Consortium, supported by BRGM (PI: J. Langlois). The primary aim of Intelligent_Mapping is to develop Artificial Intelligence (AI) algorithms able
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accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing
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software Analyze software for new, moderately complex systems and algorithms Perform data analysis, test and debug software Develop, implement and execute plans and tests Design and apply basic data sources
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, from the quantum processor to the quantum-classical interface and all the way quantum algorithms and applications. Further information on the Department is linked at https://www.science.ku.dk/english
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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a