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Field
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Position Details Position Information Job Title Submersible humidity sensor researcher Appointment Type Student Employee Job Location Corvallis Position Appointment Percent 100 Appointment Basis 12
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, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault diagnosis, and early fault prediction in electric vessels
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depletion, toxic algae, and pollutants. This natural sensitivity makes them powerful bio-sensors for environmental monitoring, capable of providing early warnings of ecosystem stress. However, harnessing this
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-constrained drone platforms. Design custom PCBs for lightweight, low-power drone platforms, including sensor interfaces, power regulation, embedded processors, and communication modules. Develop power-efficient
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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system ROS/ROS2, PX4, LiDAR, Stereo and Monocular vision, Perception, AI, ML, Path Planning Control, Sensor Fusion Algorithms;; - Previous experience in Deep Learning with LiDAR and vision systems
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satellite sensors. - Collaborate with data scientists and software engineers to operationalize algorithms and integrate them into cloud-based platforms. - Produce high-quality technical reports, maps, and
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sensors systems, 2D/3D cameras and LiDAR, inertial sensors, radar technologies, GPS, and calibration methods Real experience in designing, implementing and testing of robust, efficient perception algorithms
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between the simulator and sensors located in the operating room. This will require implementing data acquisition algorithms and data processing algorithms within the simulator. The second point concerns