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Princeton University

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PhD Position in Causality-Aware Path Learning (Network Science and Machine Learning) Princeton University in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Dec 21, 2026

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Country

United States

University

Princeton University

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Keywords

Computer Science
Environmental Science
Mathematics
Civil Engineering
Python Programming
Statistics
Physics
ML

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About this position

PhD position available at Princeton University in causality-aware path learning, with a strong emphasis on network science and machine learning.

The position is within Prof. Dr. Jürgen Hackl’s Complex Infrastructure Systems Group in the Civil and Environmental Engineering environment. This is a research-oriented doctoral opening for candidates interested in computational methods, graph/network analysis, and data-driven learning approaches for infrastructure systems.

Eligibility highlights: a Master’s degree in computer science, engineering, mathematics, physics, or a related field; strong analytical skills; Python proficiency; and preferred experience in machine learning or network science.

Location: Princeton University, United States.

Deadline: 21 December 2026 (with a Fall 2027 start mentioned).

Interested applicants should follow the posted application route and submit materials before the deadline.

Funding details

Available

What's required

Master's degree in computer science, engineering, mathematics, physics, or a related field; strong analytical skills; proficiency in Python; experience in machine learning or network science is preferred.

How to apply

Apply now via the post's application link or portal. Review the position details and submit the required application materials before the deadline of 21 December 2026.

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