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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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interfaces, and condensation surfaces under varied operating scenarios. Develop and refine performance simulation models and predictive tools to support system optimization and deployment strategies. Prepare
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quantitative ecology, applied statistics, or a related field with strong background in statistics and model development. Experience with R and analyzing spatial datasets. Ability to apply quantitative methods
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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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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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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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genomics, AI-driven modeling, and translational oncology, analyzing large-scale multi-omic datasets from pediatric cancer cohorts. The position offers a unique opportunity to collaborate closely with
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related to materials. The successful candidate will independently lead a project focused on developing generative AI models to establish structure-property relationships for materials discovery
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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