PhD student (65%) AI methods for Superresolution Microscopy16.07.2020
The Research Group “Computational Image Analysis” at the Center for Computational and Theoretical Biology (CCTB) at the University of Würzburg, Germany, invites applications for a
PhD student position (TV-L 65%, 3 years).
We are looking for a highly motivated candidate with a master’s degree in biology, physics, computer science or a related discipline, with a solid background in image processing and quantitative data analysis. Knowledge of at least one programming language and basic statistics is required. Ideally, you already have experience with machine learning and deep neural networks. If your background is not biology, you should have demonstrated interest to apply your computational skills to biological questions (e.g. in your undergraduate research).
In this DFG-funded research project, you will combine deep learning and particle averaging to improve the performance of single molecule localization microscopy, in collaboration with the Sauer Lab (inventor of dSTORM) at the Biocenter of the University of Würzburg.
You will have access to high-end computational resources, including virtual workstations with GPUs for deep learning. The Center of Computational and Theoretical Biology is a young and dynamic research unit within the Faculty of Biology at the University of Würzburg and provides excellent working conditions in a highly vibrant interdisciplinary environment. In our group, we develop and apply computational tools to analyze, quantify, and understand biological image data (“Bioimage Bioinformatics”), in close collaboration with experimental groups within the faculty.
The University of Würzburg is an equal opportunity employer. As such, we explicitly encourage applications from qualified women. Severely handicapped applicants will be given preferential consideration when equally qualified.
Please email your application documents and include a cover letter describing your research interest, a full CV and the contact information of at least two referees to Philip.Kollmannsberger@uni-wuerzburg.de