Two Postdoctoral Research Fellow positions are available in the Department of Statistics & Data Science at the National University of Singapore (NUS). These positions are offered on a two-year contract basis, with the possibility of renewal subject to review.
Although the research themes are flexible, our primary focus is on the design and analysis of statistical methods for data assimilation of high-dimensional time-series data and generative modeling. With the recent advances in diffusion-based models, concepts initially developed for the inference of time series and in the control theory literature can now be adapted for generative models. Conversely, the latest methodological developments in generative models (e.g., denoising diffusion, stochastic interpolants, flow-methods) can be applied to enhance the performance of traditional statistical tools used in data assimilation for high-dimensional time series models (e.g., ensemble Kalman filters, particle filters). In this project, we propose to explore the intersection of these two areas, aiming to enhance the robustness and accuracy of methods for data-assimilation of high-dimensional dynamical systems, as well as to develop new sampling algorithms.
Details:
Start Date: Immediate
Duration: 2 years (Renewable for up to an additional year)
Salary Range: S$ 70k-90k / Year, plus benefits (Health Insurance / etc…)
Travel: Generous budget for attending conferences
Requirements:
Ideal candidates should have:
a 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
a demonstrated record of academic publications
research experience that aligns with the core focus of the research programme.
Contact:
Queries about this post should be addressed to Alex Thiery at a.h.thiery@nus.edu.sg. We highly encourage prospective applicants to get in touch informally to discuss further details about the position first.
The positions will remain open until we find the right candidates.
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