PhD Studentship: Bayesian Modeling of High-dimensional Structural Data

The University of Manchester; Manchester; Hybrid; GBP 18,000 - 20,000; Part time

Job summary

The University of Manchester invites applications for a PhD Studentship: Bayesian Modeling of High-dimensional Structural Data. The project focuses on developing a comprehensive Bayesian learning framework for broad problems in biology, social science and engineering.

Hybrid locations include Birmingham, Bristol, London, Blackburn, Redcar or Doncaster, with the NIoT leading the initiative. Applicants should hold a 2.1 honours degree or Master’s in a relevant science or engineering field.

Qualifications

• Applicants should hold or expect to obtain a 2.1 honours degree or a Master’s (or international equivalent) in a relevant science or engineering discipline.

Responsibilities

• Develop a comprehensive Bayesian learning framework for high-dimensional problems focusing on practical applications.
• Work on scalable computational methods with theoretical properties relevant to the project.
• Collaborate with researchers across disciplines and contribute to academic outputs.

Skills

Bayesian modeling

High-dimensional data

Education

2.1 honours degree or Master’s in a relevant science/engineering field

Job description

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PhD Studentship: Bayesian Modeling of High-dimensional Structural Data

Organisation The University of Manchester

Organisation The University of Manchester Locations

Manchester, UK

Application Deadline 92 days remaining

We recommend that you apply early as the advert may be removed before the deadline.

In many applications such as biological sciences, social science, and engineering, we encounter high-dimensional observations. Bayesian approach can provide a flexible modeling framework for underlying structures in high dimension such as underlying covariance structure, conditional dependency graphs etc. With the change in data-generating mechanism, these high-dimensional structures may change with time, where the change can depend on latent factors or variables.

These projects will focus on developing a comprehensive Bayesian learning framework for this broad class of problems while focusing on specific applications. The goal would be to develop computationally efficient and scalable Bayesian learning methodologies with practical applications and establish relevant theoretical properties.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master's (or international equivalent) in a relevant science or engineering related discipline.

The National Institute of Teaching (NIoT)

Hybrid - Birmingham, Bristol, London, Blackburn, Redcar or Doncaster

Department for Environment, Food and Rural Affairs