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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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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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communication infrastructure design and evaluation, reliable communication technologies, protocols and algorithms, internet of things and wired/wireless communication at all network layers. The candidate should
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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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control strategies based on Model Predictive Control (MPC) and real-time sensor feedback to dynamically adjust process parameters and recipes. The research will integrate data-driven models, material
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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
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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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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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develop research projects for the Internet of Things for Precision Agriculture (IoT4AG) research grant Develop novel semantic mapping algorithms in the field of agricultural and forestry robotics to promote
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position