PhD student in computerized image processing with focus on analysis of tissue samples

Are you interested in developing computational tools and learning strategies for understanding health and disease at the microscopic scale? Would you like to be part of a research team with skilled and friendly colleagues in an international environment? Are you seeking an employer that offers safe and favorable working conditions? If so, check out the following PhD position at Uppsala University. The Department of Information Technology holds a leading position in both research and education at all levels. We are currently Uppsala University's third largest department, with 350 employees, including 120 teachers and 120 PhD students. Approximately 5,000 undergraduate students take one or more courses at the department each year. You can find more information about us on the Department of Information Technology website. The project will be led by Professor Carolina Wählby, within the Image Analysis unit of the department's Vi3 division, working alongside researchers developing numerical and computational methods with a particular focus on deep learning and image analysis. The research is done in close collaboration with the BioImageInformatics Unit of SciLifeLab. SciLifeLab is a national resource of unique technologies and expertise available to life scientists, closely intertwined with a community of researchers in areas such as biomedicine, ecology and evolution. SciLifeLab brings scientists together across traditional boundaries and fosters collaborations with industry, health care, public research organizations and international partners. More information about being employed as a PhD student at Uppsala University can be found here. Project description Digital pathology and detection of cancer based on hematoxylin and eosin (H&E) stained tissue samples has made enormous progress in the past ten years thanks to artificial intelligence, mainly in the form of deep convolutional neural networks. In parallel, functional analysis of tissue samples via novel microscopy techniques and spatial omics has made great leaps in terms of multiplexing capabilities and power to decipher spatial patterns of molecules and cells. They provide insight into cell development, micro-environment interactions, and transformation into diseased states. Yet, combining AI-based analysis of H&E data with spatial omics is only at its very early stages. The purpose of this project is to bridge this gap through development of computational strategies combining digital pathology and function into Functional Pathology, with focus on cancer development. In this project, the successful candidate will conduct basic research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning is essential. A successful candidate should also have a keen interest in collaborating with life scientists and learning more about pathology and tissue analysis. The project is financed by the Swedish Research Council.

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