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Field
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is driven by the increasing introduction of renewable energy sources (RES), both in large-scale installations (wind farms, solar) as well as distributed over large number of small-scale assets (e.g
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detection; security of distributed systems, security of computer networks, privacy of networked systems, human-automation interaction and operation on networked systems and infrastructure; AI and computer
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Packages/Procedures: Update, review, manage, and run daily, monthly, and quarterly reporting packages, with a keen eye on data quality and robustness, and distribute them to relevant parties within
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. Prototype autonomous systems for in-space manufacturing and testing. Develop AI algorithms for navigation, guidance, and control. Lead collaborative research proposals for NSF, DoD, DOE, NASA and with other
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; Develop models and algorithms for energy-aware scheduling, workload prediction, and performance–energy trade-off optimization; Investigate network system energy efficiency, including traffic scheduling
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Responsibilities: Conduct individual research within the designed project: process data, develop research methods, build and evaluate computer vision and machine learning algorithms empirically. Author research
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management, cache optimization, and vectorization techniques. Strong understanding of algorithms and data structures, especially those suitable for parallel processing and distributed computing. Understanding
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 13 days ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment
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and/or statistical algorithms to classify building and land-use types relevant to electrical consumption Label and prepare training data for AI models; develop automated pipelines for classification
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks