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Job Responsibilities:Provide Subject Matter Expertise, in depth analytics and recommendations in support of LTE and VSAT transport layer (RF and networking) particularly in the context of internet application user experience in a hybrid transport technology.
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The ERDC is working with a number of U.S. Department of Defense partners to advance the practices by which machine learning models are developed and leveraged for engineering modeling and simulation.
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7 years of related experience with a Masters' degree in Optics, Optical Engineering, Applied Physics, Computer Science, Software Engineering, Machine Learning, Systems Engineering, Mechanical Engineering, Electrical Engineering, Computer Engineering, or related degree.
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A graduate degree (Masters/PhD) in computer science, computer engineering, electrical engineering, mechanical engineering, aerospace engineering, or similar technical discipline with an emphasis on artificial intelligence and machine learning.
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The Machine Learning Engineer will work both individually and in concert with modeling and simulation engineers, human-machine interface developers, various subject-matter experts, and program management to design and implement suitable machine learning models, and to assist in the development of efficient, robust, and reliable intelligent system applications for several customers across government and industry.
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Comprehensive technical expertise and a research track record in one or more of the following domains: artificial intelligence/machine learning; advanced cybersecurity methods and technologies; computer science and engineering; software engineering; modeling and simulation; model-based systems engineering; and/or computational science and engineering.
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The Modeling and Simulation Data Scientist will support the Combination Product Operations organization by improving the way Client manages and utilizes data to enhance data analysis and decision making within the organization.
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Minimum of 2 years experience in research and development in at least one of the following areas: machining dynamics, machining, machine tools, sensor development, manufacturing technology.
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Experience in structural health monitoring (SHM), non-destructive evaluation (NDE), and non-invasive testing,+ Experience in electrical measurements, building of complex experimental setups, and sensor development,+ Experience in advanced signal processing, including machine learning,+ Proficiency in software development using Python, MATLAB, and LabVIEW,+ Experience in modeling & simulation studies using COMSOL,+ Ability to plan and organize assignments to meet project deliverables.
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Degree : Bachelor's or Master's degree in a relevant discipline (e.g., Computer Science, Data Sciences, Decision Science, Engineering, Information Systems, Information Technology, Internet of Things, Applied Math, Machine Learning, Quantum, or other technology majors.
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The postdoctoral fellow will be part of a cross-disciplinary research team in (Computational Science & Engineering, and Electrical & Computer Engineering, (led by Felix J. Herrmann) addressing challenges of seismic monitoring of Geological Carbon Storage (GCS), wave-based imaging and [inverse] problems, and machine learning.
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There are various disciplines involved in Enterprise Data & Analytics, including: data source identification and analysis, data engineering, data visualization & data science/machine learning.
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Emerging Technologies Group: Covers a wide range of work including Internet of Things (IoT) penetration testing, intelligent automation, artificial intelligence/machine learning and quantum computing.
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Successful candidates will play a vital role in supporting the development of software encompassing physics-based modeling and simulation, machine learning applications, digital scene injection applications, system integration, testing, and engineering analysis for a hypersonic missile defense program.
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The NCSIS portfolio encompasses modeling and analytics for transportation logistics and supply chain security; critical infrastructure cybersecurity; artificial intelligence/machine learning for cybersecurity, nonproliferation, and other relevant missions; autonomous cyber defense and situational awareness; cybersecurity workforce development; and knowledge discovery and management systems, among others.
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