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and tires. The use of such fibers requires new compounding strategies to optimize compound performance. In the proposed study, both dipped and undipped short-cut fibers will be incorporated into tire
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter
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critical infrastructure—are increasingly exposed to cyber-physical attacks and uncertainties. These disturbances induce complex, time-evolving performance degradation that requires tightly integrated
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Universiteit Amsterdam welcomes applications for a two-year Postdoctoral position in Reinforcement Learning for Stochastic Optimization. The candidate is expected to conduct high-quality research
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of performance, as they are very cautious by design. This, in turn, makes them less practical for problems where speed is of utmost priority. On the other hand, offline learning, such as Deep Learning, often
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contribute to the development and optimisation of experimental assays. This position suits someone who enjoys being in the lab, can work with growing independence, contributes with creativity and wants
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and decentralised data-center capabilities to optimize urban performance. This project aims to explore how telecommunications networks and urban infrastructures interdepend and co-evolve, and to
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Knowledge of alternative propulsion systems (hydrogen, electric, hybrid) Familiarity with ATM concepts, airspace design, or traffic flow management Experience with optimization or operational research methods
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Work Activities The Photonic Forces group at AMOLF is looking
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference