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development pertaining to chemical and biological detection. You will gain experience with algorithms, data analysis, Deep Learning, Python, pytorch and /or tensorflow, NLP, genetic algorithm, computer vision
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, model compliance, and integration with operational systems. You will support digital project delivery and the University's Smart Campus initiatives, integrating spatial and sensor data to enhance building
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DSLR cameras Familiarity with hardware interfacing and sensor integration Working Conditions / Work Schedule Work hours are fairly flexible throughout the week Posting Detail Information Posting Number
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Australian National University | Canberra, Australian Capital Territory | Australia | about 22 hours ago
to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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, cognitive science, art history, and creativity studies exploring digital interfaces that facilitate collaboration between humans and AI algorithms. We are located at the Department of Management at Aarhus BSS
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processing algorithms from concept to implementation. Eligibility to obtain a United States SECRET clearance (or higher) is required for ongoing employment in this position Minimum Qualifications: 1
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of the following areas, soft robots, legged robots, mechanism design and machine design, actuator design, electric motors, motor controller, embedded system, electronics, firmware, biomechanics, IMU sensors, Nvidia
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algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement learning. Deep
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-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from