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the following areas: deep learning, reinforcement learning, imitation learning, robot perception, navigation, and manipulation. Experience with whole-body control, humanoid or multi-DOF platforms, and
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Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D Imaging, Recommender
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, collaborative, and able to work well with adversity and ambiguity. You should have strong problem-solving skills, excellent communication skills, a deep technical understanding of modern cybersecurity threats
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Neutral Infrastructure (dfCO2), this role contributes to Program 4: Machine Learning for Carbon Performance (https://dfco2.org.au/program_4 ) that aims to advance the next‑generation AI methods to model
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multimodal generative AI for music/audio. Check out some of our recent projects here: https://dorienherremans.com/biblio (including work on models like Mustango, SonicMaster and other generative systems
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the use of effective applications and services for teaching and learning. We drive a culture that is forward-looking. With a strong passion for IT, our people are always striving to improve, push boundaries
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learning and deep sense of purpose. Our students, faculty, and staff are driven by the idea that their work will impact the world. Brown University offers flexible work/life balance; summer hours, winter
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systems. Ths position requires a deep understanding of X-ray Absoprtion Spectroscopy and prior experience with methods of machine learning and artificial intelligence. A highly competitive candidate would
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maintenance work in terms of identification, classification and analysis of work to optimize resource allocation, preventative maintenance planning and overall maintenance performance. This involves a deep
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staff position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the