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Field
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an authorized DoD SkillBridge partner and welcomes all transitioning military members to apply. You will: Design algorithms for vehicle guidance and control in an undersea environment, including signal
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(AI) and machine learning (ML) methodologies. The position involves annotating clinical data, collaborating with AI/ML/NLP teams, developing algorithms, and generating insights to improve patient care
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to the development of remote sensing algorithms, simulation and modeling capabilities, and artificial intelligence/machine learning (AI/ML) methods in support of the goals of the joint NASA/USGS Landsat mission
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to science and engineering domains. Design, develop, test, benchmark, deploy, and tune agentic AI software frameworks and multi-fidelity optimization algorithms to close the feedback loop for automated
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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principles problem formulations, algorithm development, systems designs, software implementations, and collection methods for specialized engineering applications, employing expertise in electrical and/or
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Working experience in algorithm design and implementation in software (e.g. C/C++, MATLAB, python, etc.) Understanding of data analysis, statistics, probability, and machine learning applied to IQ signals
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Working experience in algorithm design and implementation in software (e.g. C/C++, MATLAB, python, etc.) Understanding of data analysis, statistics, probability, and machine learning applied to IQ signals
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. Design, develop, test, benchmark, deploy, and tune agentic AI software frameworks and multi-fidelity optimization algorithms to close the feedback loop for automated scientific discovery. Deploy, optimize
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 13 hours ago
the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data. Major regulatory science gaps include lack of methods for AI algorithm training with limited