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methods (e.g., deep learning, generative models, representation learning) ● Experience working with large public biological datasets/repositories (e.g., GEO, SRA, UK Biobank, GTEx, etc
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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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attribution, representation analysis, causal probing, and mechanistic circuit analysis. The postholder will also develop predictive models using modern deep learning frameworks (e.g., PyTorch) and evaluate them
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. The Regenerative Immunology lab is currently composed of three PhD students, three postdoctoral fellows, one MS student, and one animal technician. The lab resides within the Division of Molecular Medicine and Gene
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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, Physics or a closely related field. Completed PhD in one of the above or a closely related field. Strong background in Machine Learning and Artificial Intelligence, including interest in alternative
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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testing methodologies for physical modelling of deep-water offshore wind turbines. You should hold a relevant PhD (or be near completion) and have a strong publication history. A strong background in
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knowledge of deep learning and computer networking/systems is required Experience with AI as a platform and expert use of AI as a tool strongly desired Experience implementing a language model is a plus Refer