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materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning
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learning environment. The instructor maintains accurate records, assists with program development and recruitment, and models the professional standards expected in the nursing field. This position is funded
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samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models, and to develop user-friendly tools that will be used by
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climate will warm and recover in a net-zero future. As part of this project, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques
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to oversee research activities outlined in NSF Grant 2520154 “Understanding Expectation-Driven Learning in Early Childhood: An Experimental and Computational Investigation,” under the supervision of Dr. Kimele
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-based ecosystem modeling, MRV/CDR, nature-based solutions, or food–energy–water systems. Experience with machine learning, remote sensing, or near–real-time environmental data systems. Experience
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engineeringEducation LevelPhD or equivalent Skills/Qualifications We are seeking a scientist with: Expertise in image-based biological tissue modeling and simulation Good command of deep learning Expertise in coding and
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programs and ability to learn new ones. Commitment to inclusive excellence. Pay Range Minimum $27.30, Midpoint $32.50, Maximum $37.70 Salary is based on related experience, expertise, and internal equity
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efficiency. Experience working with R or Python data science tools to analyze and visualize health care data and to develop data pipelines to support machine learning model development. Experience working with
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: Education: Bachelor in Biosciences, or Engineering degree in Computer or Data Sciences. PhD in bioinformatics, data sciences, machine learning or related areas. Experience: previous experience working with