Overview: The NSF-Simons AI Institute for Cosmic Origins (CosmicAI) invites applications for two Postdoctoral Fellow positions in Artificial Intelligence (AI) for scientific discovery. Each position offers an opportunity to contribute to the development of foundational AI methodologies and their application to data-intensive challenges in astronomy and physics. The Institute’s overarching goal is to create transparent, interpretable, and scientifically grounded AI systems that enable robust inference, accelerate discovery, and deepen our understanding of the Universe. These positions focus on developing methods that are explainable, trustworthy, and aligned with scientific reasoning. The Fellows will join an interdisciplinary community advancing research in symbolic reasoning, explainable and interpretable AI, optimization, Bayesian inference, and experimental design, all in the context of complex scientific data.
These positions are designed for early-career researchers who wish to bridge the gap between AI methodology and scientific application – developing novel theoretical insights while contributing to practical tools that support next-generation astronomical surveys and simulations. Fellows will have the freedom to pursue independent lines of research while collaborating closely with statisticians, computer scientists, astronomers, and physicists at the University of Texas at Austin and across the national CosmicAI network.
Applicants must specify for which position they wish to be considered. Each applicant may only apply for one position.
Advisors: Dr. Alessandro Rinaldo and Dr. Arya Farahi
This position focuses on foundational and theoretical research in AI, emphasizing symbolic reasoning, interpretability, optimization theory, Bayesian inference, and experimental design. The Fellow will pursue new methodological advances that enhance the transparency, rigor, and reliability of AI systems used in scientific contexts. The ideal candidate will have a strong background in mathematical and statistical theory, algorithmic development, or the foundations of machine learning.
Example research areas include:
Bayesian inference and uncertainty quantification
Symbolic and hybrid symbolic and statistical reasoning
Explainable and interpretable AI theory and methodology
Experimental design and information-theoretic approaches to data efficiency
Optimization methods for large-scale, non-convex, or domain-specific problems
Advisor: Dr. Arya Farahi
This position focuses on the development and application of advanced AI models for complex scientific data, particularly in astronomy and physics-informed learning. The Fellow will contribute to creating and deploying interpretable, generative, and physics-constrained models that enable trustworthy scientific inference and discovery. The ideal candidate will have strong computational and modeling skills and an interest in interdisciplinary collaboration.
Example research areas include:
Causal discovery in scientific data
Explainable and interpretable deep learning
Bayesian modeling and simulation-based inference
Physics-informed neural networks (PINNs) and generative models
Experimental design for efficient data collection in large-scale surveys
Position Structure: Fellows are expected to spend approximately 50% of their time on Institute-related collaborative research and 50% on independent research that aligns with the Institute’s goals. Ideally, there will be substantial synergy between the two. Appointments are for two years, with the possibility of renewal for a third year based on performance and funding.
Institute and Environment: Fellows will be based at the University of Texas at Austin within the Department of Statistics and Data Sciences and will work closely with interdisciplinary teams across the CosmicAI Institute. Each Fellow will have access to state-of-the-art GPU and CPU resources at the Texas Advanced Computing Center (TACC).
About CosmicAI Institute: The NSF-Simons AI Institute for Cosmic Origins (CosmicAI) aims to grow transformative AI advances with the overarching goal to increase the accessibility of astronomy data and knowledge for researchers, students, and the public. CosmicAI leverages partnerships between academia (UT Austin, U. of Virginia, U. of Utah, UCLA), national facilities (NSF NRAO and NSF NOIRLab), nonprofits/foundations, and industry to develop capabilities that enable astronomical researchers to conceptualize, define, and execute research projects via trustworthy, efficient, robust, and explainable AI methods. By developing next-generation AI tools, the Institute aims to accelerate discoveries related to one of the most fundamental human questions: Where do we come from?
Ph.D. in Statistics, Computer Science, AI, Machine Learning, Applied Mathematics, Astronomy, Physics, or a related field by September 1, 2026.
Demonstrated expertise in one or more of the following: Symbolic Reasoning, Explainable AI, Optimization, Bayesian inference, Experimental design, Optimization, generative AI, Physics-Informed Neural Network.
Strong record of research productivity, evidenced by publications or preprints in relevant venues.
Proficiency in modern AI frameworks (e.g., PyTorch, JAX) and large-scale data analysis.
Interest in interdisciplinary collaboration with scientists in astronomy and physics (no prior background required).
Appointment Period: September 1, 2026 – August 31, 2027, renewable for up to two additional years.
Salary: $75,000 annually (for 2026–2027), plus research and travel funds.
Benefits: Information about benefits is available at https://postdocs.utexas.edu/employment/benefits.
Start Date: Earlier start date negotiable.
Applicants must clearly indicate which position they are applying for (Position 1: Theoretical and Methodological Advances or Position 2: Model Development and Data Analysis). Each applicant may apply for only one position.
Application Deadlines:
Position 1: January 10, 2026
Position 2: December 5, 2025
After these deadlines, if the positions remain unfilled, applications will be reviewed on a rolling basis until the positions are filled. Please submit your application through this link: https://utexas.qualtrics.com/jfe/form/SV_eLHMdfbKU5br0Gy
Required Documents (combined into a single PDF):
Research Statement (3 pages, excluding references) – Describe past work and future research directions, emphasizing relevance to the chosen position.
Curriculum Vitae (maximum 2 pages).
List of Publications (highlighting key relevant work).
Names and contact information of three references (letters are not required at the time of application submission).
Please note: Referees will only be contacted for letters of recommendation if the candidate is shortlisted for further consideration.
For more information, contact Dr. Arya Farahi at arya.farahi@austin.utexas.edu