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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar
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of machine learning researchers, engineers, and domain scientists. This collaborative environment offers a unique opportunity to work on cutting edge AI models, with a focus on generalization and
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learning, memory, and/or decision-making - Experience and/or interest in conducting developmental research - Experience with computational modeling approaches - Experience acquiring, analyzing, and
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity to pursue independent research as
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one or more of: computational modeling of social learning, norms, or moral cognition; cultural evolution and gene-culture coevolution; evolutionary game theory and the evolution of cooperation
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mitochondrial quality leads to neurodegenerative diseases and impairs neuronal recovery. To approach these questions we use a combination of genetics, biochemistry, cell biology and imaging in a number of models
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sorghum, using new whole genome CRISPR screening techniques that the lab has developed. The project is in close collaboration with computational approaches that model the genes and circuits that make cells
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complex designs, and conducting big data analyses using advanced statistical models. Experience in the use of various datasets, e.g., TOPMed, UK Biobank, All of Us, and/or digital health data, is also