21 python "Basque Center for Macromolecular Design and Engineering POLYMAT Fundazioa" uni jobs at Columbia University
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teams. - 20% Python Scripting: Uses Python to integrate, transform, and manipulate data from multiple internal and external data sources in support of analytics and reporting initiatives. Develops and
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, statistics, computational biology, or a related field. • Proficiency in one or more programming languages, including but not limited to Python, R, C++, Stan, etc. • Excellent written and oral
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well as pediatric malignancy. The successful candidate will have experience analyzing data, and proficiency in standard statistical software, including SPSS as well as R. Knowledge of python is a plus. If the
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applied statistics, data science, machine learning, text analysis, and familiarity with coding in R and/or Python. All applications must be submitted through Columbia University?s Academic Search and
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fluency in at least one programming language (e.g. Python, R, or similar). Background in cancer biology preferred Problem Solving Works with a team to troubleshoot computational analysis Decision Making
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knowledge of programming, including Linux, Python, and R. Candidates having background knowledge in neuroimaging, machine learning, and/or genomics/genetics are encouraged. Excellent communication and writing
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health, or related field, plus at least two years directly related experience required. Advanced knowledge and proficiency in statistical programming (e.g., R, SAS, Python, Stata) preferred; prior
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detailed documentation. • Develop models and implement program code (STATA, Python, SQL, R, SAS, Matlab, etc.). • Perform statistical analysis, including regression analysis and machine learning techniques
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. Responsibilities Responsibilities include: Importing, reconstructing, and quality checking neuroimaging data. Editing and writing bash and Python scripts to batch process MRI data using software such as FSL
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in Economics Knowledge of public and environmental economics, as well as causal inference. The ability to code in R and Python. Report writing and presentation. Cleansing and scrubbing large datasets