PhD Studentship: Rough Surface Reconstruction with Machine Learning Methods

Posted on Feb 1, 2019 in Job Opportunities in Acoustics

A 3-year  fully-funded PhD post is available in the Department of Mechanical Engineering at the University of Sheffield.

This exciting opportunity will involve developing of machine learning method and modelling acoustic inverse problems. The key goal is to use acoustic waves to recover boundary geometry and material properties.

The student will join a successful team working on acoustic problems within the Department of Mechanical Engineering and the Department of Civil Engineering at the University of Sheffield. The student will also have a support from a large network of researchers from other UK and overseas universities Support and funding will be available from the UK acoustic network (acoustics.ac.uk) to attend conferences and visit UK/overseas acoustic laboratories.

Dr Anton Krynkin is currently working on the development of acoustic reconstruction techniques that can be used to recover characteristic properties of periodic/random rough surfaces. The main application of this research is in the development of remote sensing technology that is designed to analyse the state of water infrastructure and inform the assessment of flood risks as well as flood control measures.

Dr Artur Gower develops mathematical models of sound waves interacting with complex materials, specially those whose internal structure has been scrambled. He uses these models in combination with machine learning to design new sensors that use sound waves to monitor these complex materials. Industry and real world applications include monitoring powders (as used in 3D printing) and emulsions, that are prevalent across chemistry.

Candidate Profile and Further Information

We welcome applications from enthusiastic candidates with a good MEng (or BEng/BSc or MSc) degree and with desire to advance their skills in mathematics, programming, physics and mechanical engineering. Previous experience in acoustics and machine learning is not required. Knowledge of a programming language, such as Python, will be an advantage. The project is available for UK nationals or EU nationals.

The studentship is supported by the Department of Mechanical Engineering and covers the cost of tuition fees with an annual tax-free stipend for 3 years at the standard UK research rate (approximately £14,777 in 2018/19)

For further information about this project please contact Dr Anton Krynkin: a.krynkin@sheffield.ac.uk or telephone 0114 222 7847.

To apply please use our standard on-line PhD application form and indicate on your form that you are replying to this advert or email me-pgadmit@sheffield.ac.uk for further guidance on applying.

Closing date: 17th February 2019

Website https://www.jobs.ac.uk/job/BPU553/phd-studentship-rough-surface-reconstruction-with-machine-learning-methods


Closing date for applications: 17 February 2019