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engineeringEducation LevelMaster Degree or equivalent Skills/Qualifications Electrical power systems and distribution network optimization. Machine Learning and Deep Learning applied to electrical systems. Basic quantum
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: Computer science MAIN SUB RESEARCH FIELD OR DISCIPLINES1: Distributed Algorithms – Fault Tolerance – Cloud computing JOB /OFFER DESCRIPTION We are looking for a young researcher interested in fault tolerance and
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pathophysiology and basic knowledge on current therapeutic is a must. Work experience with analysis of xenotransplantated bone marrow tissues is of especial merit, including tools to analyze tissue distribution
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
to be carried out focus on the daily operational activities of home hospitalization units, with the goal of developing optimization models and algorithms to support the planning of routes, schedules, and
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 12 days ago
Conference on High Performance Computing, Data, and Analytics (HiPC 2017). https://legion.stanford.edu/pdfs/hipc2017.pdf Visibility Algorithms for Dynamic Dependence Analysis and Distributed Coherence. Michael
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Offer Description The researcher will develop various applications, algorithms, and AI techniques for Virtual Power Plants (VPPs) within the distribution grid environment. These will include neural
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Infrastructure? No Offer Description Mission: Research and develop robotic perception algorithms that allow obtaining a 3D representation of complex environments; monitoring and presentation of results. Functions
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related definitions. Knowledge of SOTA federated learning algorithms. Knowledge of distributed optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge
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, with both academia and industry recognising its importance. Distribution is an integral part of innumerous computer systems, providing them with essential improvements to aspects such as performance and
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions