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-doctoral research position under the supervision of Dr. Chris Smith Home | Chris Smith . Our lab studies spatial population genetics, using simulation-based inference and machine learning to infer
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methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant methods, high-dimensional geometry, and structure-aware learning; and the
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applications for a post-doctoral research position under the supervision of Dr. Chris Smith Home | Chris Smith . The lab— in the Evolution, Ecology, and Behavior section—investigates machine learning approaches
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methods for differential equations and scientific machine learning; geometric deep learning, manifold learning, equivariant methods, high-dimensional geometry, and structure-aware learning; and the
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Business, Business Analytics, Machine Learning/Deep Learning/Reinforcement Learning, Advanced topics in AI, and other technical courses) in the undergraduate and master’s programs. Research in IS pedagogy is
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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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must have experience effectively teaching in an online environment and five or more years of professional (industry) experience in one or more areas of expertise listed above. A deep understanding of
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computer vision tools (e.g., MediaPipe, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch
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research position under the supervision of Dr. Chris Smith Home | Chris Smith . The lab— in the Evolution, Ecology, and Behavior section—investigates machine learning approaches for spatial population
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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