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training and inference algorithms for specific edge hardware platforms. Implement and test models on neuromorphic hardware. Contribute to research proposals and funding applications. Publish and present
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cars. • Optimise AI training and inference algorithms for specific edge hardware platforms. • Implement and test models on neuromorphic hardware. • Contribute to research proposals and funding
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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training and inference of GMMs for large, high-dimensional datasets Explore parallelization strategies to leverage modern GPU architectures Benchmark GPU-based implementations against CPU-based approaches
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This position is
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 16 hours ago
(Einstein Telescope), acceleration techniques for gravitational-wave inference, including machine learning, cosmography with gravitational waves (including dark energy, dark matter, gravitational lensing
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Lösungen für inverse Probleme in der diagnostischen Biomechanik beizutragen, mit besonderem Schwerpunkt auf Elastographie. Das Projekt baut auf unserem kürzlich entwickelten Weak Neural Variational Inference
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and as part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This