TELECOM ParisTech
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Fully Funded PhD in Computer Science: Graph Signal Processing and Graph Machine Learning Télécom Paris in France
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD position supported by the PARIS-funded SIGMA project. The post mentions rolling review and may close once a suitable candidate is found; no stipend amount is stated.
Deadline
Dec 31, 2026
Country
France
University
TELECOM ParisTech

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About this position
Fully funded PhD position in Computer Science at Télécom Paris and Télécom SudParis in France, within the MM Team at LTCI and the ARMEDIA Team at SAMOVAR.
The project is part of the PARIS-funded SIGMA initiative and focuses on graph signal processing and graph machine learning. This is a strong fit for candidates interested in computational methods, data-driven modeling, and mathematically grounded machine learning.
Eligibility: MSc/M2 or equivalent in computer science, data science, engineering, applied mathematics, signal processing, statistics, or a related field. Applicants should have strong mathematical foundations and practical skills in Python and PyTorch; prior background in signal processing and machine learning is preferred.
Funding: The position is fully funded. No stipend amount is specified in the post.
Application: Send your CV, one-page motivation letter, transcripts, and at least one recommendation letter by email to the listed supervisors. Applications are reviewed on a rolling basis and may close once a suitable candidate is found. The stated deadline is 2026-12-31.
Funding details
Fully funded PhD position supported by the PARIS-funded SIGMA project. The post mentions rolling review and may close once a suitable candidate is found; no stipend amount is stated.
What's required
Applicants should have an MSc/M2 or equivalent in computer science, data science, engineering, applied mathematics, signal processing, statistics, or a related field. Strong mathematical foundations and Python/PyTorch skills are required, and a background in signal processing and machine learning is preferred.
How to apply
Email your CV, one-page motivation letter, transcripts, and at least one recommendation letter to the three listed supervisors. Apply as soon as possible because applications are reviewed on a rolling basis.
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