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biological markers and state-of-the-art deep learning, the research will uncover conserved cellular state transitions and perturbation response programs across biological systems. The successful candidate will
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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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landscape and evolutionary conservation of protease-mediated cleavage events remain poorly understood. Chloroplast proteins are particularly attractive candidates due to their high abundance, 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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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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candidate, who is eager to learn and has a genuine scientific interest. Extensive knowledge in and practical experience with protein expression and structural characterization is mandatory. Documented
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authority. Learn more about our benefits and what it’s like to work and grow at KTH. Trade union representatives Contact information to trade union representatives. To apply for the position Log into KTH’s
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of training in higher education teaching and learning. The purpose of the position is to develop independence as a researcher and to create the opportunity for further development. The postdoctoral position
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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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) 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