54 python-"https:"-"St"-"CIPMM---Systemic-Neurophysiology" positions at Rutgers University
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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into technical work action items. Self-motivated and able to work well with minimal supervision or in a team environment. Proficient in object-oriented programming (such as Python, Java). Experience with web
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. Develop and implement computational pipelines using MATLAB, Python, or similar platforms. Lead manuscript preparation for peer-reviewed journals and present findings at national and international
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packages, and programming skills in PHP and Python. Physical Demands and Work Environment Special Conditions Clean Driving Record. Posting Details Posting Number 26ST0561 Posting Open Date 03/23/2026 Special
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languages/software (e.g., R, Python). Prior experience conducting empirical research on questions related to the lab’s research focus. Prior experience managing others and building community. Equipment
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preferred candidate will have experience teaching data wrangling, programming in Python and base R, and industry experience in data analytics or data science. The role will cover the full range of duties
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options Employee and dependent educational benefits Life insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits
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. 2 years of relevant experience. Certifications/Licenses Required Knowledge, Skills, and Abilities Advanced programming skills with SAS, SQL, Stata, R and/or Python. A strong command of Microsoft
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advanced signal processing and time–frequency analyses of neural recordings. Develop and implement computational pipelines using MATLAB, Python, or similar platforms. Lead manuscript preparation for peer
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such as R, SAS, ArcGIS, SQL, Python and AI tools. Conducts geospatial and epidemiologic analyses relevant to the catchment area to assess cancer outcomes, spatial patterns, temporal trends, and disparities