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University Of Strathclyde
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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
Full funding availableDeadline
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
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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