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Field
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-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness of algorithms used in community-driven fact-checking
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process diagnostics with closed-loop process control of AM platforms, and work with fellow researchers to implement machine learning algorithms based on in-situ data collection and analysis. Minimum
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designing DNA, RNA and proteins to create nanoscale devices for applications in biotechnology and medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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. Key responsibilities include: Data Analysis: Assist in collecting and analyzing student performance data to inform the recommendation engine. Algorithm Development: Contribute to designing, prototyping
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the “Inverse Problem” component of the project: developing and validating a satellite-based pancake-ice detection algorithm using ICESat-2 wave-damping observations within a Bayesian inversion framework
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to algorithms with actionable performance guarantees. More specifically, the research will revolve around the following theme: High probability convergence in stochastic optimization under heavy-tailed noise
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in the Phi_Lab, led by Dr. Azeem Ahmad, and will focus on the development of advanced reconstruction algorithms and next-generation quantitative optical microscopy and tomography systems for imaging
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and conferences. Proven experience in design and implementation of deep learning algorithms. Outstanding programming skills in Python. Extensive experience working on one or more of the following areas