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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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and contextualize experimental findings. Apply CAD tools such as ANSYS and SolidWorks to produce geometric models for experimental specimens, including metamaterial and lattice structures as needed. 2
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of generative AI and biophysics, the Fellow will focus on expanding the current framework to model dynamic protein ensembles. As an Empire AI-funded fellow, you will have access to the Empire AI Alpha and Beta
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AI models to automate QA/QC processes, enabling real-time verification, anomaly detection and consistency checks across records. Develop a cloud-based verification platform for digital data collection
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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limited to: Computational and applied mathematics; artificial intelligence for sciences (especially biomedical science, brain science, and neuroscience); data science and machine learning; modeling