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Field
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prototyping for real-world use-cases, and building TRL-4/5 prototypes and actively optimizing the engineered solutions. The candidates will work in a multidisciplinary environment consisting of PhD-level
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Infrastructure and Cloud Operations Manages and optimizes the C-BRAIN data infrastructure: storage accounts, computes resources, data lakes, and access controls. Implements and maintains data access controls and
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proteins for high quality alternative protein foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use
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to optimize efficiency, performance, and innovation. Essential Duties Oversees development, management, and operations of research capabilities (laboratories, facilities, staff) and financial performance
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encompassing data-driven electrolyte screening, molecular dynamics simulations, and experimental validation to bridge the gap between AI predictions and practical battery performance. The optimized electrolyte
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, and experimental validation to bridge the gap between AI predictions and practical battery performance. The optimized electrolyte formulations are expected to satisfy the strict requirements of high
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optimizations, substrate biodegradation, and microbial carbon metabolism. Collects, processes, and analyzes experimental data using microbiome, metabolomics, molecular biology, and statistical or bioinformatics
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prototypes and actively optimizing the engineered solutions. The candidates will work in a multidisciplinary environment consisting of PhD-level scientists, research engineers, graduate research students and
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | 2 months ago
of Christina Lopano. This project will be hosted by the NETL Pittsburgh, PA campus. This project presents an educational opportunity in additive manufacturing to facilitate the optimization and integration
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infrastructure development. The successful candidate will collaborate closely with researchers to understand scientific challenges, develop and optimize AI/ML workflows, neural networks, and deploy scalable