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materials recovery,CO2 electrolysis and fuel cells. Experimental work will involve design, characterization, and degradation studies of model interfaces that can help elucidate their degradation mechanisms
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to recruiting high-quality academics and professionals to contribute to its dynamic, quality-driven research environment. Job Description The Energy4Water (E4W) at UM6P is seeking applications for two
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 days ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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data to identify proteomic signatures and develop novel predictive models for Alzheimer’s, Parkinson, and Dystonia as well as to identify novel proteins and pathways implicated on disease pathogenesis
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(Surgical Science Section, Division of General Surgery, Department of Surgery) is seeking a productive, organized, and self-driven postdoctoral fellow with a strong background in immunology, experience with
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collaboration, and is driven by the opportunity to create real-world solutions to complex energy challenges. You will lead modelling, optimisation and feasibility studies, engage with collaborators and
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, New York Postdoctoral Associate: Quantifying the emerging risks of wildfire smoke for birds of North America The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science
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solve research challenges and model development, as applicable. Contribute discrete components of a larger project under the general direction of a senior or principal researcher. Prepare complete
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must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials, or generative AI
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accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly detection on graphs