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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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) training personalized computational models in new contexts, and (iii) studying in-silico clinical intervention strategies. The postdoctoral fellow will have the opportunity to: Learn about computational
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. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in time and space, how this affects
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Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently Teaching and mentorship abilities or interests in personal development A
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written English is required. The applicant should be capable of working both independently and as part of a team, with problem-solving skills and an openness to learning new methods. Assessment criteria
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includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity