46 coding-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at National University of Singapore
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and processing (e.g., coding or scoring of surveys). Perform literature review and communicate learnings to the lab. Analyze and visualize data in R, Matlab, or Python.
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pipelines for data pipeline code; apply version control best practices. • Monitor pipeline health, set up alerting for failures, and respond to incidents. • Contribute to infrastructure-as-code
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degree in mechanical/aerospace/civil engineering with strong expertise in experimental and computational fluid dynamics. The candidate must have experiences in coding, designing and developing flow
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into existing workflows and operational processes. 4. AI-Assisted Development and Reusable Utilities Use AI coding tools such as Claude Code, Cursor or GitHub Copilot to accelerate development. Build reusable
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. Set delivery standards for code quality, testing, documentation, release readiness and post-launch sustainment. Support the use of AI coding tools to accelerate development while ensuring appropriate
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communications and announcements Validating and publishing learning events in teaching feedback system (Blue System) and assisting tutors with feedback QR codes Computation of teaching hours (tutor & students
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. Radionuclide dispersion modelling The migration of radionuclides, in the air or water bodies (both surface and underground) will be modelled using either community dispersion models or in-house-developed codes
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, Statistics, or a related field. • Coding: Proficiency in Python (NumPy, Pandas, Scikit-learn). Knowledge of C++ or SymPy is a major plus. • Machine Learning: Strong understanding of machine learning
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translation: Code and analyse qualitative data, and turning findings into compelling visuals, narratives, and accessible outputs for stakeholders Writing and dissemination: Draft research reports, policy briefs
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responsibilities of the position include: Supporting the development and maintenance of teaching platforms and environments used in data science and AI courses. Managing code, data, and model lifecycle workflows