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Pennsylvania State University

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Postdoctoral Scholar in Probabilistic Methods in Machine Learning at Pennsylvania State University Pennsylvania State University in United States

Degree Level

Postdoc

Field of study

Computer Science

Funding

Full funding available
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Country

United States

University

Pennsylvania State University

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Keywords

Computer Science
Deep Learning
Mathematics
Stochastic Processes
Gaussian Processes
Random Matrix Theory
Statistics

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About this position

Pennsylvania State University is advertising a Postdoctoral Scholar position in the Department of Mathematics within the Eberly College of Science at Penn State in State College, Pennsylvania, United States.

The project begins in January 2027 and focuses on probabilistic methods in machine learning, including random matrix theory, Gaussian processes, and applications to deep neural nets. The successful candidate will work with Professor Leonid Berlyand.

Applicants should have recently completed a Ph.D. and demonstrate strong research potential. A background in probability, especially random matrix theory and stochastic processes, is expected. Experience with Matlab or Python for numerical simulations and basic knowledge of neural networks are helpful but not required. Teaching may be required.

This is a full-time term position. The post notes that continuation beyond the initial term depends on university need, performance, and/or availability of funding. Penn State also mentions a competitive benefits package for full-time employees, but no stipend amount is provided.

To apply, external applicants should use the Penn State Workday portal. Required materials include a cover letter, CV, publication list, research statement, teaching statement, and at least three reference letters sent directly to saz11@psu.edu; one letter must address teaching ability. The application is complete only after all materials are submitted.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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