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Computer vision for Physical AI Vision-language(-action) and visual foundation models, multimodal learning Generative AI methods for the aforementioned fields The successful candidate will lead a new
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methods capable of identifying manufacturing defects and predicting downstream performance outcomes of Diesel Particulate Filter (DPF) units. Combining computer vision, machine learning, deep learning, and
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and Artificial Vision, UR 7535) is a research unit of the University of Burgundy Europe (UBE), recognized for its work at the interface between computer vision, image processing and control of physical
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, including rigorous experimental design, model evaluation, and scientific publication. Expertise in at least one relevant area, such as time-series modeling, computer vision, representation learning, NLP
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to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee The candidate is required to carry out research in embodied AI, especially focused on vision
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: Bachelor’s degree in Computer Science, Software Engineering, or a related technical discipline. AI & Machine Learning: Strong background in AI model training, computer vision, image processing, and
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statement detailing short-and long-term goals. 5. A vision statement for the Machine Learning and Sensing Lab detailing short-and long-term goals (identify any synergies with personal research program). 6. A
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possess ability to work in a standard office setting and to use standard office equipment, including a computer Ability to maintain attention to detail despite interruptions Ability to read printed
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. The research project focuses on the design, development and validation of Artificial Intelligence and Machine/Deep Learning models applied to healthcare, with particular reference to computer vision applied
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Military College of Georgia. More details on the UNG Mission, Values, Vision, and Culture can be found at https://ung.edu/about/mission-vision-values.php Offers of employment are contingent upon completion