University of Birmingham
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PhD in Trustworthy Machine Learning and AI: Robustness, Privacy, and Data Heterogeneity University of Birmingham in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Competition funded PhD project for students worldwide. The scholarship committee usually meets in January each year, and applicants are advised to submit well before then. No stipend amount or tuition details are stated in the post.
Country
United Kingdom
University
University of Birmingham
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About this position
PhD opportunity at the University of Birmingham in trustworthy machine learning and AI, with a focus on robustness, privacy, data heterogeneity, distributed learning, differential privacy, sampling algorithms, probability, and statistics.
The School of Mathematics is advertising two theoretical PhD project directions under the broad theme of trustworthy machine learning and AI:
1) Robust and private learning in heterogeneous and distributed environments, including network and tensor data, data heterogeneity, privacy, and contamination in a unified framework.
2) Differential privacy of sampling algorithms, including privacy properties of sampling, more sophisticated sampling schemes, subsampling effects, and relaxed assumptions on target distributions.
This is a competition funded PhD project for students worldwide. The post does not list a stipend amount or detailed tuition coverage, but it is explicitly funded. The project is described as primarily theoretical, so applicants with strong mathematical/statistical backgrounds and interest in machine learning theory, AI robustness, and privacy are likely to be a good fit.
How to apply: email your CV, transcript, and the title of one paper from the reference list to [email protected]. If shortlisted, you will be asked to give a presentation based on the paper you selected. The page advises applicants to submit well before January, when the scholarship committee usually meets.
Institution: University of Birmingham, School of Mathematics, Birmingham, United Kingdom.
Funding details
Competition funded PhD project for students worldwide. The scholarship committee usually meets in January each year, and applicants are advised to submit well before then. No stipend amount or tuition details are stated in the post.
What's required
Applicants should have strong fit with theoretical machine learning and statistical methods. The post asks interested candidates to send a CV, transcript, and the title of a paper from the reference list; shortlisted applicants will prepare a presentation on the selected paper. The project is aimed at students worldwide and is funded; no specific degree class, language test, or GPA requirement is stated in the post.
How to apply
Email your CV, transcript, and the title of one paper from the reference list to [email protected]. If shortlisted, prepare a presentation on the paper you selected. Submit well before January when the scholarship committee usually meets.
More information can be found here
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