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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
). Probabilistic and reliability-based analysis applied to underground structures. Advanced subsurface characterization techniques integrating geotechnical and geophysical data. Geohazard mapping and modeling
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
, or Julia) Experience in statistical modeling and probabilistic analysis Ability to model Argonne’s core values of impact, safety, respect, impact and teamwork Preferred skills, abilities, and knowledge
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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using probabilistic methods. You will collaborate with domain experts across transport modeling, machine-learning, and policy design to ensure scientific and practical relevance. You will contribute
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physics simulations OR designing, implementing and training machine learning models OR probabilistic reasoning and uncertainty quantification • Experience in conducting original research as demonstrated
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weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches to account for uncertainties. Use data assimilation techniques to combine observational data with AI models
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. The work is performed in connection to two ongoing research projects “Probabilistic multiscale modelling of the macroscopic crack growth behavior in heterogeneous materials” and “Optimized Digitalization
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modeling. The ideal candidate will be responsible for developing and applying probabilistic models to advance time-series analysis. Key areas of focus for this position include: 1)Probability Theory and