Postdoc in advanced methods for food quality inspection using X-ray imaging and artificial...

Internal disorders in fruit and vegetables can cause extreme losses during postharvest storage. Since the product normally has to be cut to detect internal disorders, they are often only observed upon quality inspection of the whole batch after shipping. This typically leads to refusal and subsequent destruction of the whole batch which may cause large financial losses. Recently, non-destructive imaging technologies (e.g., MRI, 2D X-ray imaging or 3D X-ray CT) have been introduced for quality evaluation of fresh produce. Detection of several disorders, macroscopically, was possible to some extent with these techniques. However, the use of non-destructive techniques for online quality evaluation of fruit and vegetables is not yet commercially feasible, due to the often low-value/ high volume of fruit.

In this postdoc project you will assist the team in the development and application of advanced methods of X-ray imaging (including multispectral and phase contrast imaging) in combination with novel methods of machine learning for image processing and analysis in industrially relevant applications of food quality inspection. You will be responsible for fundamental and applied research projects with both national and international partners, including companies, assure timely execution of research tasks with the team, assist the team’s daily operation and increase our high quality research output including high impact scientific publications.