79 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Argonne
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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, Physics, or Chemistry. Knowledge of experimental fluid dynamics. Knowledge of mechanical engineering concepts and procedures. Computer and programming skills, including data processing and manipulation
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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations
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with a team. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Experience in machine learning/deep learning methods
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with computer-aided design software. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and
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machine learning expertise, with the goal to enhance predictive capability and scalability of multi-scale and multi-physics simulation codes. Perform high-fidelity CFD simulations of complex physical
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in spatial analysis and data visualization Computer programming skills relevant for data manipulation and analysis Experience with creating and using complex data-driven analytical models using R
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), widely utilized for characterizing biomacromolecules, polymers and other nano-scale structures. The successful candidate will join an interdisciplinary team and leverage machine learning or artificial
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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the Argonne Leadership Computing Facility (ALCF), the Mathematics and Computer Science Division (MCS), the Computational Science Division (CPS), and the Data Science and Learning Division (DSL