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) and reproducible research practices Desirable criteria Experience working with generative models or large language models Experience with large scale GPU-based model training and cloud computing
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively with
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for improved interpretability and generalization. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability to work collaboratively in interdisciplinary and cross
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(LLMs); Configure and optimize cloud computing solutions or on-premise infrastructures that ensure high availability and scalability; Implement tools for efficient resource management, such as GPU
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with physical-based and machine learning methods in atmospheric science. Proficiency in Linux-based high-performance computing systems, GPU-based programming and prevailing AI tools. Experience in urban
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will develop computational electromagnetics codes for rapid characterization of the fields scattered from artificial metasurfaces. Key Responsibilities: The key responsibilities of the Research Fellow
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Simons Foundation, Center for Computational Astrophysics Position ID: Simons Foundation -Center for Computational Astrophysics -FSRFCCA [#28474] Position Title: Position Type: Fellowship or award
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has also been developing physics-based machine learning algorithms for three dimensional seismic modeling, imaging and inversion using high performance computation including parallelization on GPUs