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at the Science for Life Laboratory in Stockholm, a centre for large-scale life-sciences. NRM has a close collaboration with nearby Stockholm University, which includes joint supervision of PhD students. NRM has
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within the subject area, including experimental work, data analysis, interpretation, presentation and dissemination of research result. Specifically, the research work will involve working with cell line
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shared by Karolinska Institutet, KTH and Stockholm university. Your mission We are seeking a highly motivated postdoctoral researcher to join our team to develop new tools to analyze the sequencing data
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information about us, please visit: www.dbb.su.se . Project description The candidate will develop machine learning (ML) strategies, primarily revolving around interpretable ML and generative AI, to study
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University’s third largest department, have around 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
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biology to pioneer research in immunology using single-cell and spatial transcriptomics data. The focus will be on development of novel computational methods for gaining fundamental insights into healthy
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of further development. The postdoctoral position includes a combination of experimental work, data analysis, as well as interpretation and presentation of research results. The main part of the work for the
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the lab and bioinformatic/statistical analyses of next-generation sequence data. The project will be conducted in collaboration with Prof. Göran Arnqvist (Evolutionary Biology Centre, Uppsala University
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spectroscopy. The scholarship is two years full time with access 15 April, 2026 or by agreement. The deadline for applications is 20 February, 2026. Departmental specific information The scholarship is placed
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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