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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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join a growing, interdisciplinary team whose projects span laboratory-level and nationally significant DOE missions. Essential Duties and Responsibilities Develop novel machine learning algorithms and
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for photocatalytic degradation and H2 production.Designing multi-objective optimization algorithms to maximize environmental and economic performance. Your RoleAs a PhD researcher, you will: Build and validate hybrid
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diverse working day is guaranteed! During the project, you will develop and implement self-learning control algorithms that balance computational demand and modeling precision. You will evaluate, interpret
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Join TU Delft and work together with NXP to build low-power AI accelerators for self-healing analog/RF calibration, fixing noise/offset. Co-design algorithms & hardware and validate on real silicon
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transportation systems may include a fleet autonomous cars, vans, and buses. This PhD position within FlexMobility will focus on the underlaying assignment and routing algorithms for real-time operation of
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, data-driven algorithms, deep reinforcement learning The Pprime laboratory is a CNRS Research Unit. Its scientific activity covers a wide spectrum from materials physics to mechanical engineering
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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clustering algorithms. 2) M20 / Duration 3 months/ SCM (Amsterdam (NL) (supervisor at hosting institution: Stan van Gisbergen) Goal: acquire experience on specific features of BAND and new VCD code. Training
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Exactly: A Bayesian Approach. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration