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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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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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to support reliable DAS interpretation and urban infrastructure monitoring. Key Responsibilities: Develop automated, uncertainty-aware algorithms to infer and map fiber-optic cable routes from DAS-derived
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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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operate at the forefront of science, aiming to uncover new pathways and methodologies that will shape future ERA. We develop advanced and integrated models and approaches at both local and landscape scales
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report on findings and opportunities for clinical and operational improvements Develop and maintain supporting documentation of measurement methods and algorithms, data sources, and reporting processes
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interactions Physics-informed deep learning/hybrid modeling/reasoning AI algorithm development and optimization Job Requirements: Master qualification degree in ecology, biogeochemistry, Earth/soil science
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remains largely unexplored and represents a fascinating frontier for molecular computing. The development of new methods will be necessary to enable the manipulation of data within complex molecular
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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