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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas
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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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. More information about Dr. Card and the lab’s research can be found by visiting https://www.hhmi.org/scientists/gwyneth-card About the Mechanical Engineer role: You will be a general-purpose engineer
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/ ROS 2 for real-robot software integration. Proficiency in Python and C++, with good software engineering practices (version control, testing, maintainable code). Familiarity with robotics simulation
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S. Lawful
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Visualization, Diversity Sensitivity, Energy Sectors, Executive Summary, Graphic Design, Greenhouse Gas Accounting, Microsoft Excel, Oral Communications, Python for Data Analysis, Quantitative Analytics, Service
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communication skills in written and spoken English. Proficiency in programming with high-level languages like Python. The following will strengthen your application: Familiarity with open quantum systems
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: Proficient with mass spectrometry, quantitative proteomics, cell surface proteomics, concepts of automated sample preparation, scripting with R and/or Python. Preferred Education: Master's degree If any degree
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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented