134 coding-"https:"-"https:"-"https:"-"https:"-"https:"-"CSIC" positions in Singapore
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operations, maintenance, contract administration and project delivery. Strong knowledge of ACMV systems, asset lifecycle management, contract administration, local engineering codes and operational risk
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hands-on experience in specific scope Familiarity with required scope (e.g. national codes and standards) Good written and oral communication skills (if applicable) Proficiency in hard skills / job
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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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interviews with selected participants. Transcribe interview recordings into written text. Code, organise, and analyse research data. Prepare summaries of key findings for discussion with the Principal
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interpret quantitative findings; Be involved in making classroom observations and coding of both quantitative and qualitative data from observed lessons; Conduct post-observation interviews with teachers, and
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responsibilities Possess professional values that aligned with the Code of Ethics of SAC Strong interpersonal and communication skills Proficiency in IT skills (e.g. Microsoft 365, use of Apps, appointment booking
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initiatives. Working closely with business stakeholders, their primary responsibility is to understand business needs, document, develop, tests and implement low-code solutions to automate business processes
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relevant experience Curriculum vitae or résumé including academic background and any prior research or project experience (Optional) Code samples, GitHub profile, or links to prior projects
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(architecture principles and ML librairies) ML-literate: comfortable reading scikit-learn and PyTorch code and working with tabular machine learning workflows. Comfortable in a small team with direct ownership