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Yinan Yu

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PhD Position in Physics-Guided Foundation Models for Time-Series Data at Chalmers University of Technology Chalmers University of Technology in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

PhD position/hiring announcement; funding details are not specified in the post.

Deadline

Oct 1, 2026

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Country

Sweden

University

Chalmers University of Technology

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Keywords

Computer Science
Electrical Engineering
Artificial Intelligence
Robotics
Physics
ML

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About this position

Chalmers University of Technology in Gothenburg, Sweden is hiring a PhD student in the Department of Computer Science and Engineering.

The project focuses on physics-guided foundation models for time-series data, with applications in safety-critical systems and automotive applications. The research spans the full pipeline from fundamental machine learning questions to training on real-world data and validating methods in industrial settings.

This opportunity is a strong fit for applicants interested in machine learning, foundation models, time-series modeling, and physics-informed learning. The post emphasizes rigorous research and a desire to push beyond standard model application.

Eligibility highlights: a strong foundation in machine learning, interest in time-series and physics-guided learning, and high motivation for research. The post does not list detailed funding, language, or degree prerequisites beyond the PhD opening itself.

Location: Chalmers University of Technology, Gothenburg, Sweden.

Deadline: October 1, 2026.

Apply: use the application link provided in the post for more information and submission details.

Funding details

PhD position/hiring announcement; funding details are not specified in the post.

What's required

Highly ambitious PhD candidate with a strong foundation in machine learning, an interest in time-series modeling and physics-guided learning, and motivation to do rigorous research. Preference is for someone who wants to push the boundaries of machine learning rather than only apply existing models.

How to apply

Review the project information and submit an application through the provided application link. Check the lab website for more details about the research group and position.

More information can be found here

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