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electrophysiology (neural recording, EMG, or related techniques) Exposure to sensor design, bioinstrumentation, or embedded systems Programming experience (e.g., MATLAB, Python, or similar for data acquisition
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with electrophysiology acquisition and analysis (for example spike sorting, LFP analysis, population analyses) Strong quantitative skills and programming experience (MATLAB and or Python), including
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(for example spike sorting, LFP analysis, population analyses) Strong quantitative skills and programming experience (MATLAB and or Python), including reproducible analysis workflows Familiarity with
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scientific, word processing, spreadsheet or statistical software applications or program platforms including R, SAS, Python, and MPlus. Acquires Specialized Training. Attends trainings and skill development
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the number of years post-PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes
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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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, 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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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
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Qualifications: Exceptional programming and analytical skills (including R, Python, and SAS or Stata Demonstrated expertise in analysis of claims data Clear scientific writing and communication, an ability to work
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cellular neurophysiology or related fields Experience in patch clamp-based electrophysiological recording from native neurons Expertise in programming with Python and R Experience with programming