Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
/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
-
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