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space)? What are appropriate descriptors of spatial distribution in the field of materials science (e.g., Voronoi tessellations, particle-particle distances, etc.)? What are appropriate algorithms
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classical computing algorithms are NP-hard or, in general, difficult to implement. Within your application, please provide a research proposal (no more than five pages) answering the following questions: What
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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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. 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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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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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 16 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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Postdoctoral Researcher position. Part-time 80 hours/month distributed unequally, gross hourly salary of 80 lei, fixed period until June 30, 2026. The project is entitled “A Study of Consumer Trust in Online
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning