"Research Fellow (Statistics and Data Science)"

Job Description

The successful candidate will work with Dr. Doudou Zhou on the development of novel statistical and machine learning methods for analyzing complex modern data under a project on "Novel Methods for Analyzing High-Dimensional and Non-Euclidean Data".

This project aims to develop rigorous, scalable, and interpretable methods for real-world healthcare datasets such as electronic health records (EHR), which are often high-dimensional, multi-source, multi-modal, and incomplete. Methodological focus areas include reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, graph neural network, change-point detection.

The main responsibilities of the position include:

1. Conducting original research on statistical inference and machine learning for high-dimensional and/or multi-source data;
2. Developing and implementing algorithms for federated learning, generative modelling, and representation learning;
3. Preparing manuscripts for top-tier journals and conferences in statistics, machine learning, or biomedical informatics;
4. Contributing to mentoring graduate or undergraduate students when appropriate;
5. Assisting with grant reporting and collaborative project coordination.

Qualifications / Discipline:

• A Ph.D. in Statistics, Biostatistics, Computer Science, or a related quantitative discipline;
• Strong foundation in statistical theory, machine learning, or computational methods.
• Experience working with real-world biomedical or healthcare data is an advantage.
• A demonstrated record of academic publications.

Skills:

• Proficiency in programming languages such as Python or R.
• Ability to design and implement statistical or machine learning algorithms.
• Excellent analytical and problem-solving skills.
• Strong written and verbal communication skills.
• Ability to work independently and collaboratively in a multidisciplinary environment.

Experience:

• Prior research experience in at least one of the following areas would be preferred: high-dimensional statistics, reinforcement learning, multi-source/modal data, electronic health record (EHR) data analysis, or federated learning.
• A strong publication record (or demonstrated potential) in peer-reviewed journals or top-tier conferences.
• Experience working with real-world biomedical or healthcare data is an advantage.

More Information

Location: Kent Ridge Campus

Organization: Science

Department: Statistics and Data Science

Job requisition ID: 31566

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