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Field
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Responsibilities • Lead AI/ML algorithm development for predicting plant water and nutrient uptake under varying environmental and growth conditions. • Analyze multi-source data, including aerial and rootzone
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(iii) contributions to the commissioning of ETpathfinder suspensions, including the setup and tuning of active control algorithms. The most successful sensor prototypes are foreseen to be deployed in
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autonomy. Influences others regarding policies, practices and procedures. Essential Functions: 60% of Time the Research Scholar may: • Lead development of deep learning algorithms capable of identifying
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absorption assessment via FDTD using EM field data Detection and localization algorithms of dynamic users using wireless signal parameters and sensor fusion Generation of Digital Twin city model that is able
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the setup and tuning of active control algorithms. The most successful sensor prototypes are foreseen to be deployed in ETpathfinder seismic filters and could potentially be used for the stable recycling
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, retrain the stimulation-control algorithm for stroke-specific gait, and evaluate usability, comfort, and fit during prolonged use. You will also initiate adaptable, IMU-based stimulation control
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wearable-sensor (IMU) data and human motion analysis. Solid programming skills (ideally, in Python) for algorithm development, control design, and data analysis. Confidence and interest in testing and
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are met. Undertake these responsibilities in the project: Develop and deploy multimodal AI algorithms for fire, smoke, and hot-work detection by fusing optical, thermal/infrared, LiDAR, RADAR, and gas
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optical systems, force plates, EMG, etc.), as well as devices for field-based and real-life motion analysis and activity monitoring (sensors, wearables, computer vision, digital tools, etc.). Perform motion
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will participate in the different stages of the research cycle, from problem analysis and algorithm development to its implementation and experimental evaluation on a real robotic platform:; ; 1. Study