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the identification and development of data-driven models that can subsequently be used for system optimization. The project encompasses both algorithm design and experimental implementation. Close collaboration and
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: Develop, optimize and implement strain engineering workflows for different strain types. Contribute to standardization and development of modular high-throughput workflows Create engineered plasmids and
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and process optimization to achieve safer and more efficient PFAS destruction. Your primary tasks will be to: Design and conduct laboratory-scale UV photochemical experiments Investigate
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of millions of combinations of control parameters, determining the optimal values to maximize stability, efficiency, and dynamic performance. The ideal candidate will have experience or an interest in
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. Assisting researchers in porting, optimizing, and parallelizing their applications for QC environments, offering guidance on software development, debugging, and profiling as needed. Creating and maintaining
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cultures. To overcome the present-day challenges of the underlying field interaction with the lossy fluids and cells, machine learning will be used to optimize the resulting electromagnetic solutions
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physicochemical properties of PFAS, thereby supporting environmental risk assessments and optimization of removal technologies. Responsibilities and qualifications The postdoctoral researcher will work on the
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: developing and implementing a data reduction pipeline for 2ES to achieve extreme RV precision; conducting laboratory testing and characterization of 2ES to ensure optimal performance and stability; integrating
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the development of additional microbiological assays, including in vitro assays for plant–microbe interactions Maintaining, optimizing, and troubleshooting assay protocols and high-throughput workflows
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Job Description Do you want to contribute to a new research field where oxide/metal hybrid devices are geometrically optimized to yield extraordinary performance, including the highest