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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 15 days ago
analysis software (QGIS, ArcGIS, or equivalent). Skills in data analysis, statistics, and data visualization. Work Plan: The selected candidate will join the Coastal Biodiversity Lab at CIIMAR and will carry
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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, KY Grade Level 45 Salary Range $47,278-78,000/year Type of Position Staff Position Time Status Full-Time Required Education BS Click here for more information about equivalencies: https://hr.uky.edu
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sample preparation for structural proteomics, i.e. wet lab work and mass spectrometry, followed by data analysis and visualization of data for users of the infrastructure. We envision that you will start
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degree in a related field plus proven/relevant experience Relevant experience teaching in an online or asynchronous format. Expert knowledge of data visualization tools such as R, Tableau, Python, D3
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communication, presentation, and counseling skills Knowledge of monitoring and recovery tools, technologies and methodologies Knowledge of data management, analysis and data visualization Competent in coding
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District on-line application to be completed at http://hr.fhda.edu/careers/ *In the application, you will provide information, which demonstrates your understanding of, sensitivity to, and respect
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, thrive in a dynamic environment, and want to be part of a team that drives meaningful impact, we’d love to hear from you! Join us and be a part of shaping the future of finance at NUS! Website: https
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, and contribute to a federally funded R01 initiative focused on the integration of spatial omics, transcriptomics, and histology data. The role emphasizes computational analysis of single-cell RNA
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generation of health data scientists. Areas of expertise include bioinformatics, computational biology, artificial intelligence, network science, Bayesian methods, spatiotemporal methods, visualization