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, solvent-based recycling process for complex plastic waste streams such as multilayer packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process
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devices and equipment to support research projects that may include use of Makerspaces, machine shops, advanced manufacturing, or supervising instructional design teams; may involve use of Solidworks
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information, please see here: https://www.unr.edu/physics Contact Information Beth Jacobsen at 775-682-8787 or [email protected] Exempt Yes Full-Time Equivalent 100.0% Required Attachment(s) Please note, once
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field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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written Communication Skills. Certificates/Credentials/Licenses M.D. or PhD Required Computer Skills General office suite, GraphPad-Prism, Endnote, Snapgene, and willingness to learn lab specific software
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of AI for next-generation microbiome research? We are looking for a highly motivated researcher to develop novel machine learning and computer vision methods for autonomous live-cell microscopy
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renewable Appointment Start Date: As soon as possible, but later starting date also considered Group or Departmental Website: https://www.mignotlab.com(link is external) How to Submit Application Materials
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causal analytical foundations of mass transit system performance. The research is expected to contribute both novel methodological advances in statistical modelling, causal inference, and machine learning
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models