23 algorithm-development-"LIST"-"RAEGE-Az"-"CEA-Saclay" positions at University of Texas at Austin
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. Responsibilities Identify, research, and develop autonomy algorithms that use sonar data operating in complex environments to avoid obstacles, aid multi-agent decision making and dynamically replan current tasking
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to test and evaluate SONAR signal processing and automation algorithms developed both internally and externally (may include travel to test sites for specific test events). Implement research grade
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spectral analysis. Contribute to algorithm development and software improvements through fundamental and applied research, prototyping, validation, integration, etc. Support performance evaluations and the
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, research, and develop autonomy algorithms that use sonar data operating in complex environments to avoid obstacles, aid multi-agent decision making and dynamically replan current tasking and sorties. Design
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Job Posting Title: R&D Software Engineer ---- Hiring Department: Applied Research Laboratories ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 40 ---- FLSA Status: Exempt
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of scientific programs in Matlab or similar programming language. Interest in signal processing algorithm design and development. Interest in acoustic data processing and analysis, and SONAR system design and
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Laboratory (SISL) is seeking a motivated individual to support research and development tasks relating to software development, algorithmic development and data analytics for RF networks. This may include
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seeking a motivated individual to support and potentially lead research and development tasks relating to data science, machine learning, and algorithmic development related to RF networks. This may include
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&D Signal and Information Processing Software Developer will support software development and integration of real-time mid-frequency active sonar processing capabilities, including algorithm design
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Learning in a broad sense. Particular areas of interest include, but are not limited to, development and analysis of machine learning models for scientific computing, theory and algorithms for sampling