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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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processes. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex biological systems
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principles Selection Criteria Ph.D. in data science, computational biology, systems biology, bioinformatics, neuroscience, or other related fields Strong experience in data analysis and handling ‘omics
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microscope), signal transduction , and3D cell culture Experience with data analysis and visualization (e.g. R environment and Cytoscape; Imaris) At least one publication in relevant peer-reviewed international
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Python, for processing and interpreting complex proteomics data Familiarity with proteomics software for data analysis, visualization, and management Experience with biological samples (e.g., FFPE, plasma
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simulators. Proficiency in Python, including data handling (pandas, NumPy), visualization (matplotlib) and integration within simulation workflows. Understanding of sector coupling (e.g. P2G, P2H), energy
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analysis. Yet, there is a need for a comprehensive computational framework that can better integrate the information derived from both experimental and computational analyses and help chemists analyse
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implement methodologically appropriate analysis strategies for genetic and ‘omic data integrated into epidemiological investigations; Collaborating with external (including many international) researchers
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methodologically appropriate analysis strategies for genetic and ‘omic data integrated into epidemiological investigations; Collaborating with external (including many international) researchers to lead or
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degree in the natural/life sciences or human medicine and practical experience in healthcare/science management, especially scientific project management. Experience in data analysis and visualization is