592 algorithm-"Multiple"-"Prof" "NTNU Norwegian University of Science and Technology" Fellowship positions
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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) at Harbor Branch Oceanographic Institute/Florida Atlantic University. Currently, SAIL is conducting multiple projects ranging from Hybrid Aerial Underwater Robotic System (HAUCS) – a cyber physical system
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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literature review, algorithm design, experiment design, model training, evaluation, and benchmarking. Work with and supervise undergraduate or graduate student assistants, guiding them in data collection
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algorithms; - Automation of the model customization process by conducting laboratory tests.; - Improvement of the data workflow for real-time processing and sharing.; - Data collection in experimental and real
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for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing
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. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems