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or embedded systems. You have experience in computer architecture and/or hardware synthesis and/or formal methods for hardware verification. You enjoy working in an applied research environment at the state
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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exploration strategies that go beyond traditional techniques such as linear programming or deterministic solvers. You will work on cutting-edge methods including: Bayesian optimization Surrogate modeling
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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the solution of the inner problem. In recent years, efficient techniques for bilevel optimization have emerged, leveraging automatic differentiation and stochastic approximations. However, these methods often
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Methods in Computer Vision, 2023. [4] S. Y, J. Sohl-Dickstein, D. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” 2021. [5] H. Chung, J
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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. Processing this response provides estimates of the local variations in acoustic pressure along the fiber, over distances ranging from 40km up to 140km with some systems. This technique, called Distributed
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or microtubules. On the other hand, we have designed physics-inspired data-driven methods aiming at estimating suitable prior regularization models via Plug&Play denoisers [SMCBF23] and/or Wasserstein GANs as
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of their career developing state-of-the-art statistical and mathematical methods to analyze epidemic data, with the aim to increase our understanding of how pathogens spread in populations, assess the impact of