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Internal Number: JR89988 Position Summary This position will primarily support the implementation of a newly funded National Institute of Mental Health R01 grant focused on optimizing a digital chatbot
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on projects related to AI-based dental prostheses design and materials optimization algorithm development, under the supervision of Professor James Tsoi. No biology, biomaterials or patient contact will be
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, troubleshooting and routine upkeep Develop, optimize and validate sample-preparation and data-analysis workflows for spatial metabolomics, lipidomics and multimodal studies Act as contact for internal and external
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the city we call home, UB is distinguished by a culture of resilient optimism, resourceful thinking and pragmatic dreaming that enables us to reach others every day. Visit our website to learn more about the
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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characterization and optimization Development and operation of continuous fermentation processes under strict anaerobic conditions Strategic contribution to platform development for metabolic engineering What you
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neural networks under symmetry constraints, their optimization dynamics, and their generalization behavior—particularly in low-data or out-of-distribution settings. The work combines formal theoretical
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for their expression in plant colonizing bacteria and integrating them into the chromosomes of appropriate chassis. Control systems will be designed to restrict expression to target plants and ensure optimal expression
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Discrete Element Method (DEM) models to simulate the charging and formation of multi-component, poly-disperse burden mixtures in the blast furnace. The goal is to optimize burden structure and permeability
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resonance imaging (MRI) experiments in living rodent models to complete the work proposed in funded grants (CPRIT, NIH, other grants). Develops and optimizes scan sequences to enable quantitative in vivo MRI