Closing Date Monday, 31st August 2020
The visual properties of architectural surfaces are often assessed by stakeholders using subjective and inconsistent methods. A large manufacturing company, has been working with the Manufacturing Metrology Team (MMT) to identify suitable surface measurement solutions for the acquisition and analysis of texture data for such applications, and to research any correlations with psychophysical measurements of such surfaces, acquired using established methods involving customer focus groups. This project aims to deliver an instrument and analysis approach which will determine the aspects of surface texture correlate to those aspects of surface texture which are meaningful to human visual perception. There is scope to apply machine learning approaches to the data analysis pipeline.
The project will be supervised by Professor Richard Leach, from the MMT, see http://www.nottingham.ac.uk/research/manufacturing-metrology . MMT is an international and diverse team that thrives on openness and coopertation – students work in teams to achieve joint goals in a friendly but professional cohort.
Please send a copy of your covering letter, CV and academic transcripts to firstname.lastname@example.org. Please note, applications without academic transcripts will not be considered.
Full fees and enhanced stipend are available.
The position is available for UK or EU candidates, but International applicants who can pay the difference between the Home and International Fees would also be welcome to apply.
Candidates must possess or expect to obtain, a high 2:1 or 1st class degree in science, engineering or computer science, or other relevant discipline.Continue reading
|Title||Psychophysics: linking physical and psychological function|
|Employer||University of Nottingham|
|Job location||University Park, NG7 2RD Nottingham|
|Published||June 1, 2020|
|Application deadline||August 31, 2020|
|Job types||PhD  |
|Fields||Industrial Engineering,   Manufacturing Engineering,   Applied Psychology,   Psychophysics,   Machine Learning  |