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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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Master's degree (or equivalent experience) or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Applied Math, Linguistics or Computational Linguistics. programming skills and familiarity with GPU based technologies like CUDA, CuDNN and TensorR and background with deploying machine learning models on data center, cloud, and embedded systems.
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The Algorithms, Machine Learning, & Data Science Group within AES Advanced Tech Team creates advanced algorithms in the fields of machine learning, artificial intelligence, signal processing, intelligent control, and other areas of electrical engineering and computer science that are of strategic interest to the AES Business Unit’s goals and roadmaps.
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The role requires proficiency in patent prosecution, with a preference for expertise in electrical engineering, software-related technologies, data science/AI, networking technologies (especially network security), machine learning/artificial intelligence, and hardware security.
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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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Software, Technology, Artificial Intelligence, Robotics, Machine Learning, Data Analytics, Education, Computer Software, Information Technology and Services, Natural Language Processing.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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We develop cutting edge sensors and mission systems, high bandwidth communication systems, large-scale simulation systems, space and satellite systems, avionics virtual machines as part of high fidelity digital twins that will improve the future of software development, airplane and flight controls, cyber security solutions, artificial intelligence, machine learning, and much more.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Publications or active peer reviewer in related journals or conference, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Experience with Artificial Intelligence, Machine Learning, and various tools and frameworks. Employ popular tools like GitHub, VSCode, Platformio, Arduino, Raspberry Pi, and others to manage, develop, and prototype software/firmware.
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1+ years of hands-on experience in one of the following technical domains: machine learning, recommendation systems, pattern recognition, NLP, artificial intelligence, or a related technical field.
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Partner with ML Engineers to troubleshoot and optimize complex machine learning and deep learning applications. Develop machine learning models at scale from inception to business impact.
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Fraud Analytics seeks a motivated and talented Lead Data Engineer (Data Architect I) to contribute to the Fraud Analytics units efforts to develop and implement algorithms, artificial intelligence/machine learning (AI/ML) models, and predictive analytics to generate insights and actionable leads to identify and respond to risks of fraud, waste, and abuse.
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Architect OpenVPX DSP systems using Annapolis Micro Systems products for applications such as Electronic Warfare (EW), Signals Intelligence (SIGINT), Synthetic Aperture Radar (SAR), Software Defined Radio (SDR), Artificial Intelligence (AI), Machine Learning (ML), and Digital Beamforming (DBF.
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The world's leading brands with Artificial Intelligence & Machine Learning technologies. In the Artificial Intelligence and Predictive side, we have more than 100 TB of historical data.
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