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Field
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-efficient methods for acquiring tree-level data to support precision forestry. About the position You will work at the interface of advanced remote sensing and practical forest management. The research
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time delays need to be compensated [6]. Accurate channel estimation is known to be a difficult task in UM-MIMO settings [7,8] as it usually relies only on pilot symbols to estimate a large number of
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description Our societies rely on computer systems, and increasingly so. Unfortunately, computer systems can be the target of malicious applications—malware. These malicious applications can be complicated
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description Our societies rely on computer systems, and increasingly so. Unfortunately, computer systems can be the target of malicious applications—malware. These malicious applications can be complicated
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 19 days ago
solving complex inverse problems that link measurements to their underlying causes. This PhD interdisciplinary programme focuses on Bayesian methods for estimating physical parameters from high-dimensional
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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-based method to approximate the CFD-revealed effects of liquid metal convection on molten pool temperature predictions. • Designing and conducting instrumented WA-DED experiments to validate the developed
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degraded ecosystems across different habitat types. This is important for establishing the extent to which ecoacoustic methods and metrics are transferrable between places. There is scope within this project
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, photon science, materials science and ultrafast X-ray science. What these data challenges have in common is that they cannot be addressed with standard computational methods but rather require modern data