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Field
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interpret research datasets, drawing meaningful conclusions using appropriate statistical methods and scientific reasoning. Develop and optimize computational pipelines, scripts, and workflows using Python
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 2 days ago
science databases. This data analysis may be performed using various programming languages such as, SAS, R, or Python. As part of this opportunity, you will be able to engage with a multi-disciplinary
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Python and AI-enabled methods to extract, classify, and summarize insights from unstructured data, including text, images, and other digital content. This work will help BSA detect trends, measure impact
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Fellow will strengthen our analytic capabilities by using Python and AI-enabled methods to extract, classify, and summarize insights from unstructured data, including text, images, and other digital
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U.S. Citizen Preferred Qualifications - PhD in Physics or a related field - Experience is required in two or more of the following areas: - frequency stabilization of laser systems - Python programming
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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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* Doctoral degree in Kinesiology, Psychology, Computer Science, Biomechanical Engineering, or a related field. A high degree of proficiency using R, Python, and/or Matlab to process and analyze time-series
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analysis using Matlab, Python, etc. • Some experience using software to automate experimental controls. Required Documents: (1) Curriculum Vitae (including completed degrees, list of publications, research
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) equipped with a cryogenic stage for surface analysis - Develop computer code (e.g. Python) for the development and analysis of optical cavities Qualifications § PhD degree in Materials, Electrical
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skills: Experience in statistical programming in languages such as R (ideal) or Python. An understanding of linear models and probability. A background in biology, with a preference for cellular biology