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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 flag

Country

United States

University

Boston University

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Keywords

Computer Science
Mathematics
Probability Theory
Computational Science
Uncertainty Analysis
Probabilistic Modeling
Variational Inference
Generative Modeling
Inverse Problem

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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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Professors

Professor at Boston University

University Name
Country flag

United States

Research Interests

Probability Theory

Uncertainty Analysis

Computational Science

Generative Modeling

Variational Inference