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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
and generative models. Develop novel algorithms for generative modeling tasks and optimize LLM/GPT-like models on large datasets. Stay abreast of advancements in language modeling and generative AI
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effective immunotherapeutic targets. Finally, we plan on continuing the path to clinical translation with further preclinical research (proteasome manipulation and optimization studies, bioinformatics
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, use of microscopes, data acquisition, inverse problems in imaging, optimization methods and image estimation and in particular knowledge in methods for super resolution structured illumination
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post-doctoral’s expertise will be strongly enhanced in the general areas of pharmacy services to specialized patient populations to optimize patient outcomes. The initial duration of the position will be
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will work closely with the Principal Investigator (PI) to manage and advance ongoing externally funded research projects, with the goal of developing and optimizing sustainable materials for commercial
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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abstraction, and (2) examining how neural replay?the spontaneous reactivation of learned neural patterns?supports human continual learning by optimizing task representations offline and guiding decision-making
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Sriperumbudur. Potential research projects include (but are not limited to) developing theory and methods for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows