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., Python packages) to streamline complex processes, such as the generation of mwTab files for metabolomics data submission. Data Management and Tracking: Design and implement data tracking systems to provide
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analytics, etc.; Be proficient in one or more statistical software packages such as SAS, SPSS, R, Stata, or Python; Exhibit a passion for student mentoring and a commitment to participating in program
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to the development and maintenance of science data processing pipeline solutions. This role suits an expert Python programmer with experience in scientific workflows and high-performance computing (HPC), who thrives
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precision medicine algorithms for critically ill patients using EHR data. We are looking for a full-time data analyst with proficiency in R and python, and the ability to take machine learning projects from
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/Qualifications • Expertise in Python, Matlab, Fortran, or C programming • Advance skills in the Linux environment and shell scripts • Good knowledge of or strong interest in database technologies
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Computer Science or Statistics or closely related degree AND experience in Big Data Analytics or Data Science with proficiency in Python. Minimum: Bachelor in Statistics or closely related degree AND 5 or more years
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framework such as Tensorflow, Pytorch, MXNet and Programming Languages such as Python, Matlab, R and/or C/C++. Demonstrated project experience related to causal inference will be an advantage. Good written
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. Required: * Experience using Linux/Unix systems required. * Experience using scripting languages such as Python and Matlab. * Familiarity with relational databases and SQL required. Preferred: * Familiarity
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and analytical skills 3. Experience in system administration of Linux, Python and Ansible type systems is a plus. 4. Experience implementing network monitoring (SNMP) and alerting is desirable. Primary
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–200) desirable, as well as experience in TEM/STEM image processing and simulation using open-source Python packages such as atomic simulation environment (ASE) and abTEM. A willingness to occasionally