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forests, and boosting methods, to analyze and predict risk-reduction measures. Conduct numerical analysis and assist with model evaluation, validation, and interpretation of results. Use R, Python, or
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candidate selection will be based on the cover letter, Curriculum Vitae, interview and contact with references. Specific selection criteria: 1 – Prior hands-on experience with R and/or Python language
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analysis using scripting languages or tools such as Bash, Python and/or R. Experience in genetic analysis or physiological data analysis; experience in both is an advantage. Experience implementing
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Classification Title: Post-Doctoral Associate in unsupervised and generative ML for chemistry Classification Minimum Requirements: PhD in Chemistry or related area Strong coding (Python) and
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Python and database skills are required. Ideal candidates will have experience building and tuning machine learning models. Writing proficiency and familiarity with technical writing is required for
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Bash and Python scripts to automate provisioning and monitoring tasks. Troubleshoots storage and file access issues and assists researchers and technical staff with data management. Additional
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activities. Monitors utilization and input/output performance, identifies bottlenecks, and assists with filesystem tuning. Develops and maintains Bash and Python scripts to automate provisioning and
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(preferred) or Python. Excellent skills in data management and analysis, data visualization, and scientific communication Desired Qualifications* The following qualifications are not required or
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-credit technical skills workshops (Excel, R/Python, Data Visualization) and professional development series for undergraduate students. Serve as the primary staff academic advisor for 150+ declared QMSS
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interpreting large amounts of data using statistical methods Knowledge of statistical packages (e.g., Python, R, SPSS, SAS) Knowledge of data collection methods (polls, focus groups, surveys) Knowledge