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Field
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that reduce raw data at the sensor level. You will develop AI and machine learning algorithms for anomaly detection, pattern recognition, and efficient data compression. To ensure practical usability
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multidisciplinary topics from signal processing to machine learning to algorithm design with a focus on sensor systems. ARL is an authorized DoD SkillBridge partner and welcomes all transitioning military members
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to dispersed systems: grains, particles, droplets, bubbles, and porous media. It also has expertise in sensor and instrumentation development. The PhD will be carried out in collaboration with the NEEL Institute
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Designing algorithms and writing secure code in either C, Python, ladder logic, Structured Text, or similar languages Securing sensors, actuators, and other connected devices Configuring Local and network
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3D integration and flexible electronics. These nano and microtechnologies form the foundation of our diverse fields of expertise, which include sensor technology, circuit design, system solutions with
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systems with sensors and suitable control/machine-learning algorithms to improve print quality and reduce material waste. Large-scale structural modules printing with mobile robotic arm and gantry system
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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with
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science. If you’re passionate about building real robots that make a measurable difference in the world, we’d love to hear from you. Your primary responsibilities are: Developing algorithms for perception
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Stud. assistant (m/f/d) - Obj. Detection in clutter and Obj. Tracking in robotic hand (in Heilbronn)
an automated evaluation pipeline. Calibrate and time-synchronize multi-camera systems with tactile sensors; define data schemas and ROS 2/Isaac ROS interfaces. Algorithm Implementation: Develop multi
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scheduling to help make offshore wind farms a reality. Job description This post-doctoral position focuses on developing fundamental algorithmic advances for dynamic planning and scheduling in multi-objective