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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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experimental and computational methods to identify molecular mechanisms leading to dysfunctional cellular states in human disease (www.camunaslab.org ). The candidate will contribute to the improve Slice-seq, a
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Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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and hardware design, and biophysical modeling. We do not expect applicants to arrive with expertise in all of these. What matters most is a demonstrated ability to learn new methods, a willingness to
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/or tissue. You have professional practical experimental experience in molecular biology work such as cloning, protein production or other related basic methods. You have strong communication skills in
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experienced team, which provides service to research projects in need of different NGS-based methods, including DNA, RNA and single-cell sequencing, as well as spatial analysis. You will work with a project
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single-cell methods in technically challenging plant systems represents a central challenge in modern plant genomics. While single-cell and single-nucleus sequencing approaches have transformed research in
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through