Postdoctoral Research Fellow (2 Positions): The Department of Statistics & Data Science at the National University of Singapore (NUS) invites applications for two Postdoctoral Research Fellow positions. These positions, which are offered on a two-year contract basis with the possibility of renewal, focus on advancing statistical methods for data assimilation in high-dimensional time-series data and generative modeling.
Designation:
Postdoctoral Research Fellow
Location:
National University of Singapore (NUS)
Details | Information |
---|---|
Start Date | Immediate |
Duration | 2 years (Renewable for up to an additional year) |
Salary Range | S$ 70k-90k / Year, plus benefits |
Travel | Generous budget for attending conferences |
Research Area:
- Design and analysis of statistical methods for data assimilation of high-dimensional time-series data.
- Generative modeling and diffusion-based models.
- Enhancing traditional statistical tools using latest methodological developments.
Eligibility/Qualification:
- Ph.D. in Statistics, Computer Science, Physics, or a closely related field.
- Strong experience in Python programming.
- Experience with at least one deep-learning framework.
- Demonstrated record of academic publications.
- Research experience that aligns with the core focus of the research programme.
Job Description:
The selected candidates will explore the intersection between advanced statistical methods and generative modeling. The project aims to enhance the robustness and accuracy of data-assimilation methods for high-dimensional dynamical systems and to develop new sampling algorithms.
How to Apply:
Interested candidates should contact Alex Thiery at a.h.thiery@nus.edu.sg for informal discussions about the position before applying. Applications will be accepted until the positions are filled.
Last Date to Apply:
Open until the right candidates are found.
Feel free to reach out for any further information regarding the positions. We highly encourage prospective applicants to get in touch to discuss additional details.