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Debdeep Pati
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Postdoctoral Research Associate in Probabilistic Modeling and Approximate Bayesian Inference Boston University in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
One-year postdoctoral appointment with possibility of renewal for a second year, subject to satisfactory performance and availability of funding. Optional teaching may be available subject to funds and approval.
Deadline
Dec 15, 2026
Country
United States
University
Boston University
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About this position
The Department of Mathematics & Statistics at Boston University is seeking a postdoctoral researcher to work with Professor Debdeep Pati on probabilistic modeling and approximate Bayesian inference. The project centers on building scalable, theoretically grounded methods for inference in complex scientific settings, combining statistical methodology, mathematical analysis, and computational implementation.
The research spans three connected areas:
- Foundations of approximate Bayesian inference, including variational inference, expectation propagation, and related approximation algorithms, with attention to convergence, approximation accuracy, and uncertainty quantification.
- Probabilistic inference for generative and simulation-based models, especially high-dimensional latent-variable models, diffusion-based generative models, and foundation models where likelihood evaluation is difficult or simulations are expensive.
- Scientific applications and inverse problems, developing robust inference and uncertainty quantification tools for physics-based simulations and high-dimensional scientific observations.
The successful candidate will work closely with Professor Pati in Boston University’s Department of Mathematics & Statistics and will have opportunities to develop independent research directions within these themes. The position is research-focused and designed for candidates interested in combining modern Bayesian computation with rigorous theory and applications to scientific modeling.
The appointment is expected to begin in Summer or Fall 2027.
Funding details
One-year postdoctoral appointment with possibility of renewal for a second year, subject to satisfactory performance and availability of funding. Optional teaching may be available subject to funds and approval.
What's required
Applicants must have a Ph.D. in Statistics, Mathematics, Computer Science, Machine Learning, or a closely related field by the appointment start date. A strong background in Bayesian inference, probabilistic machine learning, statistical theory, or numerical optimization is required. Experience with approximate inference, generative models, or scientific computing is particularly desirable.
How to apply
Submit a curriculum vitae, a brief research statement, and three letters of recommendation through the application process. Direct inquiries to Professor Debdeep Pati at [email protected].
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Prepare your application
Review drafts and confirm requirements with the university.
Professors
Professor at Boston University
United States
Research Interests
Probability Theory
Uncertainty Analysis
Computational Science
Generative Modeling
Variational Inference
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.