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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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highlights, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here
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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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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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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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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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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 master
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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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Learning for Medical Imaging and Multi-Modal Data in Cancer Research Apply for this job See advertisement About the position Position as PhD Research Fellow in Deep Learning available at the Department