Publisher
source

University of Birmingham

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PhD in Learning to Optimize for Data Science University of Birmingham in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available
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Country

United Kingdom

University

University of Birmingham

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Keywords

Computer Science
Data Science
Machine Learning
Mathematics
Artificial Intelligence
Statistical Inference
Computational Mathematics
Generative Modeling
Optimisation
Computational Imaging
Econometric
Statistics

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About this position

PhD opportunity in Learning to Optimize for Data Science at the University of Birmingham, School of Mathematics.

This project focuses on optimization algorithms for modern data science and machine learning. The research explores Learning to Optimize (L2O), where machine learning is used to automatically design or improve optimization algorithms. Topics include learning step sizes, momentum, preconditioners, update directions, proximal operators, and optimization geometries.

The project emphasizes structured and interpretable learned optimizers that combine the flexibility of machine learning with the reliability of classical optimization. It also studies theoretical properties such as convergence, stability, and generalization, alongside practical performance in computational imaging, generative modelling, machine learning, and statistical inference.

Suitable applicants should have a strong background in applied mathematics, statistics, or computer science, and a clear interest in optimization and machine learning.

Funding: Competition-funded PhD project. UK and EU candidates may be considered for college or EPSRC scholarship support. Non-UK/non-EU candidates may apply as self-funded. The post also mentions China Scholarship Council (CSC) options for Chinese candidates.

How to apply: Email your CV and transcript to Dr Junqi Tang at [email protected]. Strong candidates are encouraged to make an informal inquiry. Applications are accepted all year round.

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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