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: Computational, Quantitative, and Predictive Modeling of Root Systems. This position emphasizes integration of phenomics and other -omics data into predictive frameworks. Research areas may include: Structural
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, the Research Foundation will not discriminate in its employment practices due to an applicant’s race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status
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We invite you to apply for a 2-year postdoctoral position in the area of hybrid AI, combining modern language models (LMs) with ontologies and knowledge graphs (KGs). The position is part of a newly
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models for high-temperature structural materials with applications in nuclear reactors and other energy systems. The candidate will collaborate with ANL staff to review, validate, and enhance methods
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on performance and availability of funding. We are seeking a highly motivated and qualified individual with a Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related
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the Large Language Models (LLMs). The successful candidate will work on pioneering research projects that push the boundaries of what AI can achieve, particularly in the domain of multimodal learning. You
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to early-stage efforts focused on muscle biology and inflammatory disease. The successful candidate will apply their expertise in murine models and immunology to explore disease mechanisms, validate novel
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learning models represent information internally and how their predictions can be made transparent, reliable, and clinically meaningful. This work will involve developing and applying interpretability
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materials, while having the opportunity to shape a new research capability with broad impact across quantum networking, communications, and computing. Research Focus Design and fabricate superconducting
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on simulating nanoalloy structures to create a database for materials characterization. The main tasks include running molecular dynamics and Monte Carlo simulations to model nanoalloys under various