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Field
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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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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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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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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 14 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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Research Infrastructure? No Offer Description Mission: Develop perception algorithms that allow estimating the state of a mobile robot; monitoring and presentation of results. Functions to be developed
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is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions. The concept has lately gained increasing interest from
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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
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Location ESRIN, Frascati, Italy Description Data Quality and Cal/Val Manager for Atmospheric Sensor Missions in the Sensor Performance, Products and Algorithms Section, Earth Observation Mission
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures