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Engineering best practices, writing code that is clean, modular, and maintainable. MLOps & Cloud Proficiency: Fluent in the essential MLOps toolkit, including Git, Docker, and CI/CD principles. Have experience
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or related field Proficiency in coding, quantum computation, quantum error correction, non-Markovian noise Good written and oral communication skills Ability to work independently, excellent organizational and
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., IRB protocol, research grant administration and accounting, maintaining budgets, submitting reimbursement requests, database management, data entry, and data coding) Conducting economic evaluations
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): Experience with Infrastructure-as-Code (IaC) tools such as Terraform. Familiarity with container orchestration using Kubernetes. Prior experience working within higher education or ed-tech environments. How
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using challenging, noisy industrial datasets (e.g., anomaly detection, predictive maintenance data). Code Maintenance & Documentation: Write clean, efficient, and reproducible Python code (JAX/PyTorch
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, including clean and modular code, Git, testing, documentation and code review. Hands-on experience with PyTorch and modern NLP/LLM frameworks, especially the Hugging Face ecosystem such as Transformers
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. Claude, Copilot, Cursor) in your day-to-day engineering — for code generation, review, debugging, and documentation — with a clear sense of where they help and where they don't. Solid scripting/programming
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of Artificial Intelligence and Software Security. The role will focus on advancing the use of large language models and agentic AI systems for secure software development, vulnerability detection, automated code
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that NTU’s rules, practices and codes of conducts on intellectual property rights and academic integrity are strictly adhered to. Any other task that may be necessary for the successful delivery of the course
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Job Requirements: Preferably Bachelor’s degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. First-hand experience in neuro-symbolic methods. E.g. code generation