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cryo-electron tomography (cryo-ET). In this role you will: Develop and optimize workflows for isolation and enrichment of extracellular inflammasome assemblies across patient-derived samples and virus
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learning methods for protein design Design of enzymes using computational models Identification and optimization of plastic-degrading enzymes Experimental expression, purification, and characterization
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TheBestSteel. This project focuses on the evaluation of steels under hydrogen environments, but also on the microstructure modification to optimise their performance and mitigate hydrogen embrittlement (HE). In
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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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conditions. Work Plan Description Objectives: The main objective of this research project is to use real 2D data to optimize the workflow of FWI inversion algorithms. Methodology: 2D seismic data will be used
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. Established in 2007, it aims to promote and develop technological research hubs in the fields of materials and nanomaterials, biotechnology, microelectronics, and life sciences. Its work is focused on applied
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Processes Unit, seeking talented and motivated candidates to work on biogas production from the organic fraction of municipal solid waste. This position is part of the HUB Madrid Vuela Sostenible Project, a
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supporting the development of advanced tools and models for the design and operation of active low-carbon communities and PEDs-rich LV distribution networks. The position forms a key part of the flagship D
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
, drug loading, and release performance. Key Responsibilities * Design and fabricate microneedle array patches using CLIP 3D printing * Develop and optimize loading strategies and polymer formulations
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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