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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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governance controls Experience in Agile methodologies and ability to quickly integrate into a Scrum team Experience configuring and maintaining Salesforce security models, including roles, profiles, permission
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production, purification, and quality control workflows * Develop and optimize advanced mammalian cell culture systems for viral vector manufacturing * Perform functional characterization of engineered AAV
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Questions about this opportunity? Please email npp@orau.org Qualifications
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follow best practices, ethical standards, and consistent delivery across formats. Conduct model validation and performance evaluations in controlled environments. Participate in AI-related communities
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for integrating geothermal plants into European energy system models based on the institute`s own open-source FINE framework github.com/FZJ-IEK3-VSA/FINE . Your tasks in detail: Implementing geothermal
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learning when multiple data sources are available, mostly tailored to the case of dynamic sequential inference and probabilistic recommender systems. Emphasis will be placed on rank-based models and their
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this position, you will develop high-fidelity block-based numerical models capable of representing the geometric and mechanical complexity of historical multi-wythe masonry. Your work will involve analysing how
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Language Models (LLMs). MIDS is a two-year degree program offering a substantive educational experience, not simply a data science credential. The MIDS curriculum is designed to support students from
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at Cornell University is seeking a Postdoctoral Associate to advance research on maize and grass molecular diversity using genomic large language models (AI). The goal is to design nitrogen-efficient maize