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Field
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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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and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies. Proven expertise in machine learning and deep
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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
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research, formulate hypotheses, and design effective experimental plans. Strong programming skills with deep learning frameworks (e.g., PyTorch). We regret that only shortlisted candidates will be notified
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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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, 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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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches
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and the ability to publish high-quality academic outputs. You are expected to have proven experience in developing and applying predictive deep-learning models for data-driven analysis and forecasting
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polarisation shaping, and finally using deep learning to solve the complexity challenge associated with coherent beam combination. The role Within HiPPo, your specific task will be to develop a ‘digital fibre
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leader • Excellent written and oral communication skills Preferred Qualifications • Background in antisemitism studies • Experience with R, NLP and deep learning libraries • High performance computing