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techniques including behavior cloning, reinforcement learning, and VLA reasoning Train models that are robust to real-world challenges such as sensor noise, partial observability, contact dynamics, and
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ideas to understand how design shapes behaviour and experience. Students are introduced to key tools and platforms such as Arduino and sensors, TouchDesigner, and introductory Python programming
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/FMV, geospatial and AIS tracks, signals and sensor data, and text-based intelligence reports and develop pipelines, annotation frameworks, and quality assurance processes while gaining deep exposure
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on innovation in semiconductors, biology, machine learning, and material science toward an intelligent closed-loop system of non- or minimally invasive sensors and actuators enabling personalized and periodic
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++, and C#; design of DC-DC converters, AC motor drives, and control algorithms Sensors such as MEMs accelerometers, piezoelectric sensing, MEMs microphones, etc. Current eligibility for access
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working with image and video data covering at least selected aspects of data acquisition, quality control, dataset preparation, feature extraction, motion analysis, algorithm validation, or AI model
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303.497.3665 Karl Francis Stupic [email protected] 303.497.4564 Description Neural net systems have been developed to process magnetic resonance imaging (MRI) data from sensor space into real space and
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suites across the software stack Building and improving a hardware-in-the-loop (HIL) test bench for systems interfacing with sensors and spinal cord stimulators Defining and executing test strategies (unit
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have worked with tools, techniques, algorithms, software, and programming languages for deep learning, reinforcement learning, statistics, sensors and sensor fusion, planning, computer vision, or related
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multimodal sensor data (video, LiDAR, BIM). • Assist in the deployment and acceleration of deep learning models (e.g., TensorRT, ONNX) onto edge computing devices on the robot. • Develop interactive web