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The Department of Cell and Molecular Biology (ICM) (https://icm.uu.se) is organized into seven research programs, each focusing on distinct areas within cell and molecular biology i.e. computational
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of pancreatic cancer. This group is led by Associate Senior Lecturer Dr. Qiaoli Wang, as part of the SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS). Group members will be enrolled
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of biochemistry and biophysics, at Stockholm university. For more information about us, please visit: www.dbb.su.se . Main responsibilities The SciLifeLab and Wallenberg National Program for Data-Driven Life
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, the University of Melbourne, and the University of California, Los Angeles. We strive for all PhD students to get a solid international experience during their PhD. About the DDLS Fellows program The PhD position
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background in Machine Learning, with the ability to understand and extend current research Solid programming and engineering skills Comfortable working with modern development tools and practices Strong
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part of the SciLifeLab and the research school of the Wallenberg National Program for Data-Driven Life Science (DDLS), within the research area Cell and Molecular Biology. To achieve this, the doctoral
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methodological innovation as well as real biomedical applications. Applicants should include a personal letter and CV with information about programming skills. Eligibility requirements The position follows
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requirements, please see the subject’s general study plan . The specific requirements are met by having passed exams in areas relevant to the subjects of image analysis and machine learning with a minimum of 90
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and biophysics, at Stockholm university. For more information about us, please visit: www.dbb.su.se . Main responsibilities At the SciLifeLab and Wallenberg National Program for Data-Driven Life Science
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foreign degree. This eligibility requirement must be met no later than the time the employment decision is made. Strong written and verbal communication skills in English Proficient in a programming