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Field
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on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and Data Science, Safety and
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research • Deep learning and predictive modeling • Natural language processing and large language models for biomedical data • Drug response prediction and treatment optimization • Biomedical knowledge
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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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selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing
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Development of reproducible software tools and computational workflows for biomedical research Develop novel AI, machine learning, and deep learning methods to address complex biomedical questions in diabetes
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engineering Background in deep learning, computer vision, LLMs, visual language models, agentic AI, AI system design. Equipment Utilized Physical Demands and Work Environment Special Conditions Posting Details
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reservoir-scale heterogeneity and mineralogical variability in deep-marine lobe successions, and the role these may play in CO2 migration, pressure dissipation, and reaction front surface area to support CO2
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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related field). Decent programming skills, especially in Python or JAX. Familiarity with finance theory (asset pricing, derivative pricing, risk management etc.). Familiarity with machine learning or deep
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 13 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical