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Field
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. The successful candidate will conduct cutting-edge research on how microalgae and cyanobacteria can enhance soil fertility, optimize nutrient availability, and contribute to more sustainable agricultural practices
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products. Investigate and optimize technologies for biomass pretreatment, enzymatic hydrolysis, fermentation, and other biochemical or thermochemical processes. Investigate the potential of seaweed-derived
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experiments using gene-edited hematopoietic stem/progenitor cells Develop and optimize gene editing protocols for ELANE for therapeutic use Evaluate safety, efficacy, leukemogenicity and off-target effects
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these proteins in probiotic bacteria and experimentally test their activity Optimize protein designs based on experimental results Develop bacterial biosensors of intra- and extra-cellular analytes Active
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will work closely with the Principal Investigator (PI) to manage and advance ongoing externally funded research projects, with the goal of developing and optimizing sustainable materials for commercial
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, testing, and demonstration of novel CCUS technologies Process simulation, cost, life cycle, and social assessment of CCUS value chains Reactor design, optimization, and sizing using phenomenological and/or
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will work with data collected from the field to the spatial scale, and investigate spatial optimization approaches to improve the model parameterization at the spatial scale. We expect that you will be
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Associate. This postdoctoral researcher will work under the supervision of Prof. Bo Ji and Prof. Lingjia Liu, conducting research on immersive communication/computation-aware optimization of next-generation
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. The selected candidate will contribute to an in-depth study of organic, colloidal, biological, and inorganic fouling mechanisms, the optimization of cleaning protocols (CIP – Cleaning in Place), and the
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, establish advanced manufacturing routes for these polymers and implement computational algorithms to assist their optimization. The RISE Polymer Lab is dedicated to developing the next generation of robust