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Job Description

The successful candidate will work with Assoc. Prof. Ying Chen on a project titled "AI-Driven Analysis of Human Performance".

The main responsibilities of the position include:
- Developing and implementing reinforcement learning (RL) algorithms to model and optimize human performance.
- Designing, testing, and refining AI-driven simulation environments for dynamic performance feedback and adaptation.
- Performing data preprocessing, feature extraction, and modeling from multi-source sensor and experimental data.
- Conducting literature review and preparing research manuscripts for publication in top-tier journals.
- Collaborating with interdisciplinary teams and contributing to project documentation and presentations.

Qualifications / Discipline:
- Bachelor's or Master's degree in Mathematics, Computer Science, Engineering, or related quantitative discipline.
- Strong theoretical background in machine learning, optimization, or stochastic processes.

Skills:
- Proven experience with Reinforcement Learning.
- Excellent Python programming skills; familiarity with TensorFlow, PyTorch, or similar frameworks.
- Experience with data analytics, algorithm design, and simulation modeling.
- Strong analytical thinking and problem-solving ability.
- Good communication skills and ability to work independently.

Experience:
- Prior research or project experience applying reinforcement learning to real-world problems, especially in healthcare, human performance, or network modeling, is highly desirable.
- Publications, open-source projects, or previous research in AI, RL, or computational modeling will be considered a strong advantage