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Field
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modeling, curriculum/active learning, data selection, iterative post-training, safety alignment and guardrails. Foundation Models for Science at Scale: Pretraining, instruction tuning, continued pretraining
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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develop independent research activities on the air pollution modelling. Candidate Profile: The Center is looking for a Post-doc to work at the interface between Air Quality modeling and machine learning
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions in
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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, clustering analyses, propagating location and other uncertainties...) of mid-ocean ridge catalogs, using standard, Bayesian and machine learning techniques. ⁃ Implement methodologies that improve estimates
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10-month post doc internship in a unit other than the project provider and in a country other than the country where the doctoral degree was obtained; 2. the project leader was not the promoter
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Classification Title: Post Doc Associate Classification Minimum Requirements: Ph.D. degree in biomedical informatics/bioinformatics, computer science, data science, electronic/biomedical engineering