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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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at the intersection of mathematics and AI safety, with a focus on developing rigorous mathematical foundations for AI interpretability. Research directions include mean field theories of deep learning, data attribution
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PhD project, the successful candidate will develop an open-source workflow using deep learning and hierarchical statistical models to streamline the data flow from acoustic recorders to ecological
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, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists of three full professors, one associate professor, 6 postdocs and about 15 PhD and 7
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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FAST-TRACK – Fast Alloy Screening for Next Generation Aerospace Superalloy Development EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce Institute PhD Research
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team players and passionate on cutting edge computer vision and machine learning technologies, as well as possess deep understanding of machine learning technology and experience on turning machine
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning
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about the lab at: https://mbzuai.ac.ae/study/faculty/natasa-przulj/ and https://przulj-lab.github.io/ Qualifications PhD in Computer Science, Mathematics, Physics, Bioinformatics, or a related
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exists. See the review date specified in AP Recruit to learn whether the department is currently reviewing applications for a specific position. If there is no future review date specified, your