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Job Description

The successful candidate will work with Professor Delin CHU on data-driven research in the field of machine learning and applied data science under a project on "Regularization and Stabilization of Port-Hamiltonian Descriptor Systems".

The main responsibilities of the position include:
1. Assisting in the design, development, and evaluation of machine learning models;
2. Collecting, cleaning, and managing datasets from various sources;
3. Conducting literature reviews and benchmarking experiments;
4. Implementing data pipelines and model evaluation frameworks;
5. Preparing research reports, documentation, and presentation materials;
6. Collaborating with other researchers and supporting academic publications.

Qualifications / Discipline:
1. Master's degree or above in Data Science, Statistics, Mathematics, or a related discipline;
2. Strong academic background in machine learning, algorithm design, or quantitative analysis.

Skills:
1. Proficient in Python; familiarity with data science libraries such as NumPy, Pandas, scikit-learn, and visualization tools (e.g., matplotlib, seaborn);
2. Experience with at least one deep learning framework (e.g., PyTorch, TensorFlow);
3. Familiarity with data preprocessing, feature engineering, and model evaluation techniques;
4. Ability to manage and analyze large datasets (structured and unstructured);
5. Good communication skills, attention to detail, and ability to work both independently and collaboratively.

Experience:
1. Prior exposure to academic research, internships, or industry projects involving data analysis or machine learning;
2. Experience with any of the following is advantages: natural language processing, computer vision, time-series analysis, or multimodal data integration;
3. Experience preparing reports, presentations, or contributing to academic work during research projects.