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Field
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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: Optimize Retrieval-Augmented Generation (RAG) techniques to improve the relevance and contextual accuracy of LLM-generated content. Explore and apply multimodal LLMs capable of effectively processing and
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such as PyTorch and TensorFlow. Experience with high-performance computing and/or scientific workflow. Strong background in inverse problems, numerical optimization and image processing. Job Family
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field - expertise in applying scientific knowledge and conducting practical laboratory research Extensive experience in synthesizing and optimizing host materials for lithium-sulfur batteries Hands
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modeling tools to support outbreak response, optimal vaccine strategies, and understanding key epidemiologic questions. This NIH-funded project is conducted in collaboration with the California Department
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experience. Research background in decision making systems, in particular the use of different optimization, machine learning, and decision making modeling techniques for problem solving. Desire to grow
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- and socially- vulnerable populations and/or b) improving healthcare delivery (i.e., from providers and within the medical system) to optimize healthcare outcomes of women with uterine fibroids
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with optimization techniques and accelerated computing are a plus for this position. Strong written and spoken English skills are required. The successful candidate will: (1) develop novel methodologies
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, safety, and compliance. Perform preventive maintenance on laboratory equipment to keep them in optimal working condition. Conduct experiments related to identifying metabolites in plant and biological
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, but not limited to: Energy systems decarbonization Smart energy systems Climate mitigation strategies Life cycle analysis/optimization of energy systems Integrated assessment models of energy systems