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Field
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health, behavioural, or social science datasets. Proficiency in Python and/or R, and familiarity with open access AI/ML libraries. Track record (or strong potential) of peer-reviewed publications
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• Computational skills for multi-omic data analysis (R, Python) is a plus. Department Contact for Questions Dr. Asmaa El-Kenawi Email: asmaa.elkenawi@cancerimmunometabolism.com Website: https
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libraries (e.g. Python, TensorFlow/PyTorch, Scikit-learn) Demonstrated experience in applying machine learning or data-driven methods to physical systems, preferably in the context of fusion research Proven
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policy, economics, statistics, or a related quantitative field Additional Qualifications Strong skills in Stata, R and/or Python and experience analyzing complex data Demonstrated experience working with
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with computational environments for ’omics data manipulation (command line, Python, R, etc.) * Deep knowledge in at least one relevant subdiscipline, i.e. bioinformatics, microbiology, microbial ecology
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for analysis (e.g., text manipulation); One or more computational environments for statistical analysis (e.g., MATLAB, Stata, R, or Python); Creating and managing very large datasets; Managing and mentoring
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quantitative research methods. A familiarity with at least one statistical software package or programming language such as R, SPSS, Stata, SAS, or Python Working knowledge of AI systems and the technical and
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, Python, and SAS or Stata Demonstrated expertise in analysis of claims data Clear scientific writing and communication, an ability to work both independently and in teams, and a track record of publications
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systems or autonomous AI frameworks · Solid foundation in computational biology · Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX) · Strong analytical, problem solving, and communication
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demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must