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-assembly mechanisms, identifying robust experimental signatures of collective properties, exploring practical applications, and utilizing artificial intelligence and machine learning to aid in this process
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. Your primary focus will be on conducting high-impact
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to solving applied problems through research, collaboration and education on sustainable plant production. We teach and research plants for food, feed and energy. The research and teaching focus
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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high level of motivation and commitment, good aptitude/skills towards laboratory work and the willingness to learn. The applicant must also be organized, rigorous and be able to work both independently
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in labour unions, etc. We seek a talented and open-minded candidate, who is eager to learn and has a genuine scientific interest. Extensive knowledge in and practical experience with protein expression
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for the interview presentation. The interviews are performed by a panel of UCMR and UPSC researchers. The date of the final interview is 6-7 October 2025. Learn more about life as an ‘EC’ postdoc