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beyond the PhD in a research or engineering environment focused on large-scale AI. Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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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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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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; 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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the number of years post-PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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, Medicare and/or commercial claims data, a deep understanding of analytical methods and statistics, and advanced programming skills are therefore desired. The role will also involve preparation of graphical