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Field
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. Assist PI to facilitate research team development, optimization, and validation of in vitro mammalian cell based assays that are related to metabolic diseases (such us cell culture, RNA/protein extraction
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characterization, and functional properties of milk proteins and peptides. Our primary research goal is to understand their impact on human health. Our current projects involve developing and optimizing methods
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of design rules for 3D printed parts that are fabricated by various additive manufacturing (AM) and hybrid manufacturing processes. Development of analytic models, simulation, and optimization for the AM
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optimization for the AM processes and design automation for AM process. Development of a predictive model for AM and hybrid manufacturing processes using Digital Twins Development of 3D printing technology
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technologies. Design and optimize EUV technologies based on free electron-driven materials Lead papers and publish in high-impact journals Present at top-tier international conferences Assist PI in writing
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integration and optimized operation using machine learning and AI techniques as key drivers for improving system performance. The hired candidate will have the opportunity to work with cutting-edge energy
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efficient algorithms with provable statistical guarantees, using tools from: high-dimensional statistics, optimization, probability theory, etc. These positions would be especially relevant for those with a
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to, big data mining, protein structure and function prediction, protein-ligand interaction, molecular modeling, molecular toxicology, workflow development, gene knockout design and optimization, and genetic
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, and optimization to address challenges created by the operationalization of AI within partner organizations. The Postdoctoral Fellows will play a pivotal role within the LISH/Data and Science Operation
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, optimize performance, and assess fault tolerance under various operating conditions. Architect and implement multilayer PCB designs, ensuring optimal layout for signal integrity, power integrity, thermal