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learning, large-scale model optimization, and generalization. To explore scalable optimization methods for large-scale, distributed, and multi-node collaborative training. To conduct theoretical analysis
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key role in driving high-impact research, developing innovative objectives, and formulating proposals within cutting-edge fields, including Computer Vision, Digital Healthcare, AI Optimization, and
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of efficient AI-for-Science systems, leveraging foundation models (including small language models) and parameter-efficient adaptation techniques such as LoRA, grounded in a strong understanding of optimization
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learning are preferred. Key Responsibilities: Strong candidates should take the lead of at least one of the following responsibilities: Soil organic carbon model development and optimization, focusing
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to conduct the research on development of analysis framework on how to build up a generic framework to use learning-assisted approach to solve various optimization problems Develop mathematical modeling
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ecosystems where interconnected multi-agents interact strategically in dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently
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dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently multi-agent, strategic, and resource-constrained. Each agent has its
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
collaborative research environment that combines expertise in genomics, computational biology, and evolutionary genetics. Research Responsibilities The successful candidate will: Develop and optimize ancient DNA
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requirements at the energy edge. Further, you will incorporate compliance-by-design AI architectures and models and validate our solutions across key energy use cases such as energy market optimization (demand
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using languages such as Python, R, and Bash. · Experience developing, implementing, and optimizing computational pipelines and bioinformatics tools for large-scale genomic datasets. · Strong