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About the position
Institution: School of Computer Science, University of St Andrews
Lead Supervisor: Dr. Peter Macgregor
Full Details:
https://blogs.cs.st-andrews.ac.uk/csblog/2024/11/14/fully-funded-phd-scholarship-in-algorithms-for-data-science/
Contact: Interested applicants can contact Dr. Peter Macgregor at prm4@st-andrews.ac.uk to discuss the project.
Modern data science and machine learning applications work with datasets that contain millions of data points and hundreds of dimensions. For instance, deep learning pipelines generate large vector datasets representing text, images, audio, and other types of data. Analyzing such massive datasets with classical algorithms often demands significant computational time and resources, which might not be available in many applications.
This project seeks to develop a new generation of fast algorithms for data analysis, optimized to run in linear or sub-linear time. These algorithms typically provide approximate results instead of exact solutions. Additionally, as datasets may change over time, the project will focus on creating dynamic algorithms capable of handling updates efficiently.
The research will involve the design, analysis, and implementation of algorithms for processing large dynamic datasets. The goal is to develop state-of-the-art algorithms with either practical performance or theoretical guarantees. The work could involve analyzing existing algorithms, designing new algorithms with provable guarantees, or implementing heuristic algorithms with superior empirical performance.
Potential Research Areas
The areas of research may include, but are not limited to:
Nearest-neighbour search algorithms
Clustering algorithms, such as hierarchical clustering, density-based clustering, and dynamic clustering
Numerical linear algebra
Other projects in algorithmic data science and machine learning
Research topics will be tailored to the interests of the candidate.
Interested applicants are encouraged to contact Dr. Peter Macgregor to discuss the project further.
Requirement
The ideal candidate should have:
A strong interest in algorithm analysis
Knowledge of discrete mathematics and linear algebra
Familiarity with existing data analysis and machine learning algorithms
Strong programming skills would be advantageous
Application date
Application Deadline: 1 March 2025
How to apply
https://blogs.cs.st-andrews.ac.uk/csblog/2024/11/14/fully-funded-phd-scholarship-in-algorithms-for-data-science/
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