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analysis results integrated with their associated genomes and accumulated over more than a decade of research. Genome and protein foundation models are advancing quickly, yet almost none of that progress has
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Professor Rabin and others in formally drawing out the implications of theoretical models in both “behavioral” and “classical” economic theory. Particular emphasis will be put in psychologically-grounded
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for the Advanced Nuclear Reactor and Fuel Cycle Engineer (Staff) role. This role is responsible for working with state-of-the-art modeling and simulation capabilities for lattice physics, reactor core, spent fuel
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for the Advanced Nuclear Reactor and Fuel Cycle Engineer (Associate) role. This role is responsible for working with state-of-the-art modeling and simulation capabilities for lattice physics, reactor core, spent
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learning algorithms using Python/R. Learns to fit machine learning models on biological data. Identifies the specific datasets and machine learning algorithms. Implements the algorithms in Python/R and fit
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, create visualizations, maintain reproducible code, and assist with research reports and manuscripts. Opportunities to Contribute: Prepare, organize and quality-check spatial transcriptomics, imaging, and
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University of California, Los Angeles | Los Angeles, California | United States | about 19 hours ago
vascular biology, alongside deep expertise in myocardial biology and the molecular mechanisms of left- and right-ventricular failure. This includes but is not limited to in vitro models (isolation of adult
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reproducible project documentation, and support the careful use and evaluation of large language models and automated theorem-proving systems. A central responsibility will be to serve as a human-in-the-loop
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historical maps, soil data, and modern elevation models to locate buried wetlands and model flood risks. Independent Project Management: Manage day-to-day research tasks, troubleshoot workflow bottlenecks, and
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. The candidate will contribute to projects focused on developing advanced machine learning models to quantify phenotypic and agronomic traits of crops, including corn, soybean, and other selected species