62 algorithm-development-"https:"-"UCL" "Universidade do Minho ISISE" Postdoctoral positions at Oak Ridge National Laboratory
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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of application development techniques (numerical methods, solution algorithms, programming models, and software) at scale (large processor/node counts). A record of productive and creative research as proven by
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for transmission or distribution grids, synchronous generators, large loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component
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conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Contribute to the design, development, and implementation of new models, methods, and algorithms
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for 2D Quantum Magnets) collaboration. While located at the facility, the role is not primarily experimental; instead, the successful candidate will develop forward models, synthetic datasets, and AI tools
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photosynthesis to join the new pilot study of Generative Pretrained Transformer for genomic photosynthesis (GPTgp). The GPTgp project aims to develop a foundational holistic model of photosynthesis that will scale
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Requisition Id 15408 Overview: Oak Ridge National Laboratory (ORNL) (https://www.ornl.gov/) is the largest US Department of Energy science and energy laboratory, conducting basic and applied
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physical characterization techniques (differential scanning calorimetry, dynamic light scattering, small angle neutron and/or x-ray scattering) to characterize the DIBs; and (3) Develop/implement image
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/Responsibilities: Develop and apply AI foundation models for hydrological and Earth system modeling, with emphasis on improving predictive capabilities for compound flooding in coastal regions. Design and implement
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the aim of developing pulse-field measurements at CORELLI at SNS. This work will be conducted collaboratively with scientists within ORNL and from external universities, providing an opportunity to work in