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Field
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spectrometry-based proteomic data generated in our chemoproteomics experiments. The postdoctoral associate will be responsible for developing and optimizing custom data analysis and visualization pipelines; and
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in live or fixed organisms, cells or tissues - Experience with data analysis in Python, R or similar - Experience working with animal models AND - Experience with mass spectrometry, including sample
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spatial analysis. Establishes new research protocols and procedures, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom
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cancer and intraoperative studies. Job Duties Develops, implements and manages computational workflows and data analysis pipelines for processing and interpreting mass spectrometry and mass spectrometry
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analysis using the Digital Signal Processing (DSP) toolbox in MATLAB (including novel analyses), and data visualization via YAEL (Your Advanced Electrode Localizer). The successful candidate will also have
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, Skills & Abilities: Research Methodology & Critical Thinking: Strong foundation in research methodology, critical thinking, and problem-solving abilities. Data Analytics & Statistical Analysis: Proficiency
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). Ability to perform simple descriptive statistics, hypothesis testing, and data visualization. Modeling and exploratory data analysis (EDA) to identify patterns, trends, and insights within datasets
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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis
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, dynamic mapping, mobile application development, spatial data analysis, visualization, and GIS. The Lab conducts interdisciplinary collaborative projects with research partners on campus at the UO, with
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(in vitro and/or in vivo). • Proficiency in relevant software for data analysis and structural visualization. • Experience with in-silico discovery of antimicrobial