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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 days ago
. Forbes, S. Borkowski, S. Heidmann, and L. Meyer. Massive analysis of multidimensional astrophysical data by inverse regression of physical models. In GRETSI 2023 - XXIXème Colloque Francophone de
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composite materials (lamina properties, laminate stacking, orientation effects) is a plus. Familiarity with optimisation and/or surrogate modelling (regression, Gaussian processes, neural nets, etc.) is an
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), quantization and sharding, prompt optimization, reinforcement learning, Transformers/Deep-SSMs/Test-Time Regression. Experience with probabilistic machine learning, including but not limited to Gaussian
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, Gaussian, etc.), force-field-based simulations software (LAMMPS, DL_MESO, etc), and Monte Carlo methods (self-programming or using software). Experience or strong interest in data-driven modelling and
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. 5, no. 2, pp. 354–379, 2012. [2] C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning. MIT press Cambridge, MA, 2006, vol. 2, no. 3. [3] G. Daras, H. Chung, C.-H. Lai, Y
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strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set