The purpose of the job is to create new knowledge for academia and industry in the fields
of computational imaging, optics and machine learning. The research project aims at
jointly optimizing the optics and inverse imaging algorithms for compact camera designs
employing machine learning to achieve image quality for a certain task.

The research goals are specified as follows:

1. Develop models of refractive optics applicable to compact optics used in portable
devices that ensure realistic and manufacturable compact optics design

1. Develop ML based post-processing imaging methods to couple with the optical models
2. Develop end-to-end imaging system optimization methods to co-design compact optics
and efficient ML algorithms
3. Validate the end-to-end methodology on selected case(s) of image restoration and
enhancement

As a Doctoral Researcher you are expected to perform overviews of state of the art, to
study the principles of physical image formation and its modelling in terms of optics,
sensing and image processing, to investigate methods for optical/imaging co-design
employing modern machine learning an develop the corresponding algorithms, to generate
experimental results, analyse and validate them on use case(s) through comparison with
the state of the art.

The results of your research will be disseminated in scientific peer-reviewed journals,
international conferences, doctoral network events and events with industry. The tasks of
doctoral researchers also include teaching duties that amount to approximately 5% of their
annual working hours, unless there are justified reasons to the contrary.

At the university, the work will be supervised by Prof. Atanas Gotchev and Prof. Humeyra
Caglayan. You will also be supported by practitioners in the steering group, including
company representatives.

By participating in DSII events, excursions, meetings and training sessions with your fellow
doctoral students, you will both get and provide peer support.