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strong team dedicated to pioneering innovative experimental and modeling techniques that bridge the gap between laboratory-scale physics and field-scale implementation. Our mission centers on developing
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learning, or computational research Required Experience Demonstrated experience building, training, fine-tuning, and evaluating machine-learning or deep-learning models using established frameworks such as
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-making under uncertainty relevant to security and defense settings. Research activities will focus on methodological innovation, theoretical development, and applied mathematical/statistical modeling. Why
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motivated Postdoctoral Associate to join a research project focused on multiphysics simulations and computational modeling. The successful candidate will develop and perform finite element-based simulations
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that universities, companies, and venture capital firms don't fund because they view it as too risky. We prefer to use the word Create a Job Match for Similar Jobs About Harvard University Harvard University is
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monitoring applications. Under limited supervision, the Postdoctoral Fellow will collaborate with the PIs to design and conduct field- and modeling-based research. In this position the Fellow will: Develop
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project. · Architect and evaluate multi-site cardiotoxicity risk prediction models integrating structured EHRs, clinical notes via natural language processing, strain echocardiography features, and
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, delay thermal breakthrough, and enhance long-term thermal recovery from EGS. This position also aims to model, evaluate, and optimize innovative engineering solutions, including chemical, mechanical
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Medical Computer Vision Experience with Large Language Models and Generative AI Experience working with real-world EHR and imaging data Experience working with PACS and the DICOM standard Preferred
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Oden Institute for Computational Engineering and Sciences | Austin, Texas | United States | 2 months ago
genetic data Multimodal machine learning for biological discovery Translational genomics and risk modeling The fellow will work in an environment that emphasizes methodological innovation, statistical rigor