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Field
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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) for the high-luminosity phase of the LHC, in particular on its mechanical design, on the generation of the L1 trigger primitives, and on the development of offline reconstruction algorithms. In addition, it is
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pressure sensors, allowing them to measure the movements of the fish and detect pressure signatures in their wake. Numerical simulations were developed to predict the hydrodynamic signatures generated by
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• Develop, consolidate, and optimize fMRI and EEG neurofeedback algorithms. • Design, integrate, and test standalone neurofeedback software (software suites for clinical environments). • Contribute
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(multilayer) low-rank tensor decompositions [5,6,7], which supply a rigorous mathematical framework to develop new algorithms and model architectures and to understand their behavior. These techniques will be
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compatible with in vivo imaging still needs to be developed. In practice, the first part of this project will involve familiarizing the student with algorithms for measuring cell motility in traditional FF-OCT
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on the development of deep learning methods for reconstruction and physics analysis of the ATLAS experiment data. The successful candidate will develop innovative analysis methods for the reconstruction or the physics
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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 16 days ago
follows a phased algorithm: 1) generate an initial training set by uniformly sampling input points 2) (re)train the model on the trainng set 3) use feedback from the model’s performance to generate/augment
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into refining computational strategies for large-scale molecular simulations in materials science and computational physics. The project will involve substantial numerical development, including algorithm design
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learning. However, conventional neural networks are not well-suited to the computational constraints of FHE. The project aims to develop more efficient neural network architectures tailored for encrypted