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of agricultural in dry lands and marginal environments, animal value chain, water and energy sustainability, biorefinery and bioenergy, cultivation and conversion of micro and macroalgae, and geology and mining
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to generate data which can be coupled with robust and physics-informed machine learning and reduced-order modelling methods for model predictive control and reinforcement learning. PhD (or soon to be completed
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Posting Details Posting Details Logo Institution South Dakota Mines Working Title Postdoctoral Researcher Posting Number NFE02537P Department SDSMT-Nanoscience and Nanoengineering 1 Physical
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
Posting Information Posting Details Department Neurology - 411801 Posting Open Date 05/16/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title Post-Doc
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questions and hypotheses, mine software repositories data, build statistical models, develop methods for linking scientific software to the scientific outut it helps to produce, and write up and present
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: A small, international team with open communication and flat hierarchies Specializes in machine learning and data mining for plant genomics Committed to interdisciplinary work combining genetics, data
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of agricultural in dry lands and marginal environments, animal value chain, water and energy sustainability, biorefinery and bioenergy, cultivation and conversion of micro and macroalgae, and geology and mining
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the ResilientCrop website. He/she will also be responsible for collecting plant material at Moorepark, Fermoy with the help of either the PhD student or the Post-Doctoral Researcher. Project Title: Mining for climate
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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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, proteomics, lipidomics and metabolomics in large-scale human populations. The lab is interested in integrating and mining the different types of data to understand the genomic causes and other biomarkers as