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of antibodies/ADCs and small molecules. We are addressing combinatorial target identification with CRISPR functional genomics and chemical biology for targeting hard to treat cancers. The Aparicio Lab has a long
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contribute to the collaborative TQT research community. Principal Investigator: Na Young Kim Project Name: Solid-state analog Optimization Solver and Quantum Machine Learning (Theory) Research Area
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genomic approach to profile enhancer dependencies. Develop and optimize experimental workflows integrating scRNA-seq, scATAC-seq, and other single-cell omics. Interpret high-dimensional data in
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) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution networks (PDNs
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(HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs, GPUs, AI accelerators etc.) require high power demands with optimized power distribution
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signal processing algorithms on FPGA, optimized to significantly improve the resolution of real-time energy measurements made by the ATLAS Liquid Argon Calorimeter system. Use novel high-level synthesis
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safety and quality standards. Maintain and repair laboratory equipment, ensuring optimal functionality to support research activities. Monitor and manage inventory of materials, supplies, and laboratory
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addresses major limitations in surgical force sensing and has strong potential for commercialization and clinical impact. Key Responsibilities Redesign and optimize a fiber-optic (FBG-based) force sensor
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specimens; making solutions Optimize and develop new methods/techniques for embalming Box cadavers for shipment to distributed sites, VGH workshop sites, or funeral home for cremation Assist instructors
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond