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Monte Carlo simulated data, but there is also the possibility of working with real open data from the ATLAS and/or CMS experiments. The methods are general and applicable well beyond particle physics
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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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Uppsala University, Department of Information Technology Are you interested in probability theory, statistics, and mathematical modelling? Would you like to develop new methods for uncertainty
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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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improvement of existing workflows, while development of new analytical methods is not a primary responsibility of the role. Contract terms Temporary employment until 31 October, 2027. Preferable starting date
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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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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