University Of Strathclyde
1 month ago
Funded PhD in Novel Time Series Machine Learning Methodology for High-Dimensional Data University of Strathclyde in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Funded PhD project for students worldwide. The post indicates funding is provided, but it does not specify stipend amount, tuition coverage, or other financial details.
Deadline
Sep 30, 2026
Country
United Kingdom
University
University Of Strathclyde

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About this position
Funded PhD opportunity at the University of Strathclyde in Novel Time Series Machine Learning Methodology for High-Dimensional Data.
This project sits in the Department of Mathematics & Statistics and focuses on developing new time series machine learning methods for high-dimensional data, with emphasis on missing data imputation, forecasting, anomaly detection, and probabilistic uncertainty quantification. The research combines statistical modelling and modern AI, including state-space models, Markov regime-switching networks, factor models, deep learning, temporal convolutional networks, transformers, Bayesian inference, and quantile regression.
Application areas include financial forecasting and risk modelling, public health monitoring, and environmental trend analysis such as air and water pollution across regions. The project aims to produce publishable research, open-source time series AI models, and deployment-ready prototypes for selected applications.
Supervisors listed are Dr Jiazhu Pan and Prof Ke Chen at the University of Strathclyde in Glasgow, United Kingdom.
This is a funded PhD project open to students worldwide. The post does not specify stipend amount, tuition coverage, or other financial details.
To apply, register your interest on the FindAPhD project page and submit the enquiry form. The university will respond directly after you send your details and question.
Funding details
Funded PhD project for students worldwide. The post indicates funding is provided, but it does not specify stipend amount, tuition coverage, or other financial details.
What's required
Applicants should be interested in time series machine learning, high-dimensional data analysis, forecasting, anomaly detection, and missing data imputation. The project is suitable for candidates with a strong background in statistics, mathematics, computer science, or related quantitative fields; the post does not specify additional formal requirements such as GPA or language tests.
How to apply
Register your interest through the FindAPhD enquiry form on the project page. The university will respond directly after you submit your details and question.
More information can be found here
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