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on Wednesdays and Fridays 11:35-12:55 and labs Mondays 15:35-17:25. This course includes an Introduction to problem-solving methods and algorithm development. Emphasis is on designing, coding, debugging, and
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. Knowledge or experience of Robot Operating System (ROS), tools for training and using deep learning algorithms and in image processing and computer vision algorithms and solutions using open-source software
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quantum algorithms formulating industrial problems to make them accessible to quantum algorithms mapping quantum algorithms to specific use cases and applications optimizing algorithms in the context
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Khani. Job Duties: Developing new models, optimization algorithms, and machine learning algorithms for transportation systems and services (40%). Applying the models and algorithms to new transportation
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Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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implementing RF signal processing algorithms on edge processors including RF System-on-a-Chip (RFSoC) devices with applications in radar and/or signal intelligence. Responsibilities and Essential Duties 1
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algorithms and data structures. Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch). Ability to develop and implement AI models for business applications. Knowledge of cloud computing
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will be able to teach one of the undergraduate courses related to AI/ML, programming languages, data structures and algorithms, operating systems, network security, visualization, and human-computer
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data and machine learning processing pipelines. It will also focus on developing AI algorithms for semantically enriching data assets with domain knowledge, which feed into building knowledge graphs