The Complex Systems Science team at the College of Science, School of Physical and Mathematical Sciences, led by A/Prof Cheong Kang Hao, is looking for passionate individuals to join its interdisciplinary research team.

Vacancy: Research Fellow (AI Scientist) - Educational AI, Learning Analytics, and Immersive Simulation

The Research Fellow will lead and support AI and data science workstreams across a portfolio of projects, with an initial focus on building AI-driven learning analytics for immersive virtual laboratory simulations and personalized assessment.

Key Responsibilities:

AI and data science development

• Design, develop, and validate machine learning models for skill inference, mastery estimation, and personalized feedback, including Bayesian approaches, sequence models (for example LSTM), and reinforcement learning where appropriate.
• Engineer data pipelines to capture, clean, and analyze high-frequency interaction telemetry (for example accuracy, reaction time, and action sequences) and generate individual learning trajectory reports.
• Build evaluation frameworks to benchmark AI outputs against expert-defined competence standards and human ratings; implement active learning loops with domain experts.

Productization and platform engineering

• Develop analytics services, dashboards, and reporting tools for educators and learners, with attention to usability, privacy, and auditability.
• Implement MLOps practices (versioning, reproducible training, model monitoring, data governance) suitable for research-to-deployment pathways.
• Integrate AI components with XR or simulation platforms (for example Unity-based workflows), including instrumentation for event logging and performance analytics.
• Co-design study protocols with investigators, including quasi-experimental designs, and support statistical analysis and interpretation of learning outcomes.

Stakeholders and other contributions

• Contribute to publications, technical reports, and research dissemination/presentation; support grant reporting and documentation.
• Identify opportunities to reuse models, pipelines, and analytics modules across other domains and projects (for example healthcare, training, serious games, and adaptive learning).
• Work closely with stakeholders to translate requirements into AI specifications and deliverables.
• Mentor research assistants and student project members; review code, support experimentation, and enforce good research engineering practices.

Job Requirements:

• PhD in Computer Science, AI, Electrical Engineering, Biomedical Engineering, Mathematical Sciences, Informatics, Control, or related disciplines.
• Strong background in AI, deep learning, and data analytics.
• Experience with multimodal data integration and analysis.
• Proficiency in programming languages such as Python, MATLAB, or R.
• Experience with signal processing and statistical analysis.
• Knowledge of neurophysiological assessment techniques and behavioral analysis.
• Strong publication record.
• Excellent problem-solving skills and ability to work independently and collaboratively.
• Effective communication skills for interdisciplinary collaboration and dissemination of research findings.

We regret that only shortlisted candidates will be notified.