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for separation and performance benchmarking Develop and implement AI-driven optimization workflows (Bayesian optimization, active learning) Analyze membrane selectivity, stability, ion transport, and energy
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dynamic, interdisciplinary team of scientists focused on early-stage drug discovery research and will work in collaboration with scientists at Indiana University’s Indianapolis campus and the Indiana
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mathematical modeling framework to find the optimal operation strategy for public transport services with autonomous vehicles Conduct computer programming to verify the efficiency of the designed solution
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: This project focuses on the development, optimization, and validation of functional antibody assays to evaluate immune responses to Bordetella pertussis. This will support ongoing efforts to better characterize
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: Designing, synthesizing, and optimizing molecules targeting GPCRs (cannabinoid receptors; allosteric and orthosteric) and ion channels (α4β2 nAChR, α7nAChR, α9* nAChR, GIRK1/2, GIRK1/4, etc.) Conducting
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mathematics, economics, operations research or a related field. Additional Qualifications Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control
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protein foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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materials, the design and optimization of catalytic reactors, the study of reaction kinetics, and the detailed microstructural and physicochemical characterization of catalysts. The primary objective is to
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environment. Research Area and Environment. The successful candidate will work directly with the CMAI faculty. Broadly, the group’s activities lie at the interface of optimization and nonsmooth/nonconvex
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of structural optimization schemes for customized reinforcement configuration in reinforced concrete component. • Conduct mechanical testing on steel-concrete composite elements. • Collaborate with team