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Field
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. The post offers extensive collaboration across the Institute, supporting AI-guided design-build-test-learn workflows, biological discovery, and the development of open algorithms, benchmarks, and
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modeling and AI. This position will include: Developing new Generative AI algorithms for developing intelligent agents in areas such as planning, exploration, perception, physical reasoning, and memory
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for tumor behavior and clinical outcomes Development and implementation of artificial intelligence and machine learning algorithms for biologically and clinically motivated questions in pediatric oncology
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A postdoctoral research associate is sought to develop an experimental (hardware) platform that enables efficient exploration of complex parameter spaces. The platform will integrate comprehensive
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and porting of scalable science applications and algorithms for OLCF and other leadership computers. Debugging and tuning applications for high performance. Developing performance models of existing
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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the cellular and molecular pathways disrupted in brain disorders such as schizophrenia and autism, by utilizing recent advances in genetics and genomics. We are developing and applying tools to understand how
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mechanics, machine learning or applied/computational mathematics. ● Demonstrated ability to carry out original mathematical derivations and/or develop computational tools, evidenced by publications
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to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural problems that also depend
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developing algorithms for robot navigation, path and trajectory planning, resource allocation, coordination, or distributed decision-making. Knowledge of wireless communication systems, communication-aware