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data. Statistical analysis using R and/or Python. Reproducible computational workflows. Scientific writing and publication. Microbiome research and host-associated microbial communities. The ideal
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Argonne National Laboratory is seeking a postdoctoral researcher with expertise in life-cycle analysis, industrial process modeling, and critical minerals and materials assessment. The appointee
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HAICu project to help unlock the potential of cultural digital archives through multimodal use, providing richer context and a more comprehensive analysis of current complex issues in society. If
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record in relevant peer-reviewed journals, including publications demonstrating a substantial individual contribution. Experience with scientific programming, experimental automation, and data analysis
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datasets, with a focus on omics data. This will include implementing and applying software analysis pipelines and interpreting disease-related data together with experimental and clinical collaborators
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), statistical analysis of LHC data or beyond-the-Standard-Model phenomenology, is meriting. Experience with large-scale training on GPU and HPC systems, with design of experiments and active learning, with open
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uncertainty analysis, process optimization, and evaluation of manufacturing scale-up pathways. Develop and apply computational models, simulations, optimization methods, and data-analysis tools to evaluate
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this project, in collaboration with the two PIs (Jelmer Borst and Leendert van Maanen). We envision that you will: (1) develop a standard HMP analysis protocol and use that to analyze and label existing data, (2
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research projects related to end-of-life-care for persons with disabilities and their families. He/She will conduct advanced data analysis and prepare manuscript for the projects. He/She will also be
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unless stated elsewhere in the job posting. Work Experience: No additional work experience unless stated elsewhere in the job posting. Skills: Collaboration, Data Analysis, Data Interpretations