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Field
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(PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: the project: i. Perform fermentation and optimization of GRAS
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, develop, and optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt
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Responsibilities: Conduct programming and software development for graph data management. Design and implement machine learning models for optimizing graph data management. Conduct experiments and evaluations
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uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a comprehensive visual feature codec for machine uses
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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, the candidates will learn strategies and techniques for LC-MS analysis, drug optimization and lead drug candidate selection, pharmacokinetics and drug metabolism, clinical sample analysis and clinical
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development, validation, and optimization of 3D-printed propellers. This includes conducting hydrodynamic analysis, finite element modeling, as well as overseeing site-based test bedding, data collection, and
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yeast and fungi for food ingredient and biochemical production. The specific goals are: Metabolic engineering of Yarrowia and Candida yeasts for organic acid production Design and optimization
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optimization of these processes to achieve desired material properties and structural characteristics. Key Responsibilities: As part of the candidate's scope of work, they main responsibilities will be focusing
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. Experimentation & Implementation - Develop and implement the related algorithms, models, and techniques. - Perform rigorous benchmarking and evaluation of the developed models. - Optimize algorithms for efficiency