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The Inertial Sensors Department, 5216, performs state-of-the-art design, fabrication, and testing of MEMS based sensor technologies for national security applications. You will support advanced microelectromechanical systems (MEMS) sensor technology development in transitioning new environmental sensors from development to production rigor manufacturing for high consequence national security applications.
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Hands-on experience with technology such as sensors and signal processing, machine vision and machine learning, and internet-of-things type technology. Knowledge of autonomous sensor deployment and AI-driven machine vision.
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As a member of the Data Engineering team, the Machine Learning Engineer will work closely with business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence.
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Artificial intelligence techniques such as deep reinforcement learning, reinforcement learning, machine learning, neural networks, supervised learning, unsupervised learning, generic algorithms, Bayesian networks, fuzzy logic, etc.
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The Machine Learning Engineer is responsible for proposing, planning, executing, and analyzing research and development machine learning projects related to the field of advanced manufacturing artificial intelligence.
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Our mission and team expertise spans beyond software to advanced sensor systems, algorithms, embedded systems, signal processing, and machine learning. The candidate will join a multi-disciplinary team of scientists and engineers and work on a full stack of developing cutting edge Computer Vision (CV) and Machine Learning (ML) methods based on data from a variety of sensors.
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Specific experience with data acquisition systems, wireless technologies, robotics integration, signal processing, machine learning, mechanical design, or product development is advantageous.
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We’re looking for a Machine Learning Engineer who will build out and design machine learning infrastructure for a team of data & remote sensing scientists, working collaboratively with the Analytics and Software teams to deliver data-driven products and solutions.
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Collaborate with experts in hardware development, physics-based modeling, signal and image processing, machine learning, and embedded software engineering to develop cross-disciplinary solutions.
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Experience with popular machine learning and deep learning frameworks such as GPT , TensorFlow, Keras, PyTorch, and Scikit-learn. The ideal candidate should have a strong background in machine learning, deep learning, LLMs, and data analysis, as well as experience in developing and deploying AI models on hardware/wearables.
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We are seeking a highly skilled and talented Mechanical Engineer Supervisor with expertise in smoke sensor development and strong proficiency in smoke and airflow simulation. If you are a seasoned mechanical engineer with a passion for smoke sensor development and expertise in smoke and airflow simulation, we want to hear from you.
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To succeed in this position, one should be familiar with and understand one or more of the following: electromagnetics, communications, optics, receiver architecture, advanced signal processing techniques, sensor modeling, and/or sensor performance validation.
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We work as a team: We are a collaborative company comprised of highly-educated industry leaders in radar systems, image and signal processing, machine learning, hardware development, and software engineering.
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The Machine Learning Engineer III will be a member of the Learning and Active Perception (LEAP) group in AV’s MacCready Works division and develop a variety of innovative computer vision (detection, classification, localization, and tracking) and other analytics solutions for defense and commercial applications.
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Sources include, but not limited to, sensor alert logs, firewall logs, content filtering logs, and Security Event Manager. CompTIA A+ plus CompTIA Security+, CompTIA CySA+, or CompTIA Network.
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sensor engineer machine learning security clearance jobs Company: David Weekley Homes
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