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Field
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and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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deep learning (segmentation and foundation-model architectures) Demonstrated ability to design and execute technical projects Scientific independence with clear interest in biological mechanisms and
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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of generative AI, deep learning, and inverse problems. For Grade 7, the successful applicant is not required to have post-qualification research experience, but will hold or be close to completion of a relevant
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learning and deep learning models for early prediction. The research integrates behavioural and cognitive assessments with neuroimaging and AI, including neuropsychological testing, cognitive tasks
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, applying deep domain knowledge and advanced quantitative methods to inform critical development decisions. At Northeastern University, the Fellows will engage in scientific publication, conference
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computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability to communicate scientific results clearly through
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, computer science, data science, or similar · Strong publication record in peer-reviewed conferences and/or journals · Experience applying machine learning methods (especially deep neural network approaches
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related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core job duties include